Meenakshi is the Editor-in-Chief at The Scientist. Her diverse science communication experience includes journalism, podcasting, and corporate content strategy. Meenakshi earned her PhD in biophysics from the University of Goettingen, Germany.
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Recently, Google Photos digitally prodded me to revisit a few vacation photos taken a couple of years ago. Once I clicked “relive the day,” I saw my selfie against a scenic backdrop of the calm Chicago River and sprawling high rise buildings. Interestingly, the photo reminded me of something beyond the visible picturesque surroundings: the morning of the boat ride.
I recalled how I had overslept, gotten ready in record time, and frantically biked to just make it for the tour. I took the photo to mark the triumphant moment that I boarded the boat.
The incident got me thinking about the popular saying, “A picture is worth a thousand words.” The photo substituted for my lengthy description of the tour, but I ended up narrating a different 1,000-word story to my family: the saga behind the click.
Pictures often evoke memories of equally interesting behind-the-scenes stories. Scientific images are no exception. When researchers look through their publication-worthy figures, they no doubt have a roster of incidents spring to their minds. Some might recollect the frustrating failed attempts or the tireless efforts to troubleshoot an experiment. Others might relive the emotions of joy, exhaustion, and relief that flooded in when they finally confirmed their hypotheses.
While researchers convey their motivations, experimental methods, and scientific data through papers, there is no equivalent outlet for their human experiences. As science storytellers, we at The Scientist value the behind-the-image story just as much as the data in an image. In our new Science Snapshot column, we intend to bring these scientists’ stories to the forefront. In our first one, neuroscientist Selena Romero at the University of North Carolina narrated her experience of imaging mouse peripheral neurons in a three-compartment microfluidic chamber.
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Scientists employ live-cell imaging to capture dynamic cellular processes over time. While they can observe unlabeled cells, researchers commonly label cell structures or biomarkers with fluorophores to provide subcellular information about their samples. However, live-cell imaging presents them with several challenges, making it a complicated technique to master.
Researchers must balance acquiring high-quality images with ensuring that the cells remain healthy. Optimal imaging settings, such as high illumination intensity, long exposure times, and short intervals between images, are often detrimental to cellular health.1 Conversely, the gentle settings required for ideal cellular health frequently produce image series with inadequate spatial and temporal resolution.
Further exacerbating this problem, cellular health is also affected by environmental conditions, including temperature, pH, and nutrient or ion concentrations.2 Temperature fluctuations also cause drift, where a microscope loses its focus on a chosen focal plane, which often derails experiments.
Additionally, most microscopes acquire a separate image for each fluorescence channel. If the cells and internal structures move during this process, the images appear flawed when the software overlays the channels, which hinders spatial-temporal correlation analysis between the fluorophores.
Microscope systems, such as Mica from Leica Microsystems, overcome these challenges through innovative design and intuitive controls. When equipped with the live-cell imaging package, this automated system stabilizes the temperature, carbon dioxide levels, and humidity of the imaging environment. In addition to its drift correction and widefield and confocal modes, the instrument acquires four fluorescence channels simultaneously, producing perfectly merged images. Microscope systems like this one improve live-cell imaging by balancing cellular health and image quality.
Danielle is an Assistant Editor at The Scientist. She has a background in neuroscience and molecular psychiatry. She has previously written for BioTechniques News, The Scientist, and Drug Discovery News.
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Delivery remains a huge challenge for gene therapies. Adeno-associated viruses (AAV) dominate the gene therapy landscape, but safety and efficacy concerns have led many to question whether it is time for a change. Meanwhile, billions of people received nonviral lipid nanoparticles (LNP) via COVID-19 mRNA vaccines, but with such a limited portfolio in genomic medicines, can LNP deliver? We asked two experts to weigh in on the viral versus nonviral conundrum.
These interviews have been condensed and edited for clarity.
Many endogenously prevalent biomolecules, including collagens, hemoglobin, flavins, lipofuscin, and fatty acids, naturally autofluoresce. When performing microscopy, scientists must also contend with obfuscating signals introduced during the experimental workflow such as off-target antibody cross reactivity, antibody adsorption to the sample, and even the negative charges carried by fluorescent dyes that can promote nonspecific antibody binding. Researchers need to quench or filter out these nonspecific signals when performing immunofluorescence microscopy or staining so that they do not obscure the signal of interest.1
Scientists commonly combat nonspecific signals by using blocking and quenching agents. Blocking agents occupy potential binding sites for fluorescent dye-labeled primary and secondary antibodies, preventing them from attaching to unintended locations. However, while conventional blocking agents such as bovine serum albumin, gelatin, or casein can reduce nonspecific protein binding, they are not effective at blocking background from charged dyes. Quenching agents bind to autofluorescent biomolecules to reduce or absorb emission. However, they often emit fluorescence themselves in different portions of the spectrum.2
In light of these challenges, scientists are looking for new options to clear the way for more striking and effective staining, visualization, and imaging. One example of innovation in this area is the TrueBlack® IF Background Suppressor System, a set of buffers for immunofluorescence (IF) staining that is designed for blocking both nonspecific protein binding and background signal from charged dyes. These buffers can be used for both blocking and antibody dilution, and contain detergent for simultaneous blocking and permeabilizing in a single 10-minute step. Advances such as these offer flexible and effective solutions for countering nonspecific antibody binding and autofluorescence for scientists conducting a wide array of assays.
Tiffany earned her PhD in Genetics from North Carolina State University, where she explored the effect of genetic background on the ability to derive induced pluripotent stem cells. In March 2020, Tiffany joined LabX Media Group as an assistant science editor for The Scientist. She began working with Drug Discovery News in October 2020.
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Much like tree branches, hundreds of axons extend from neurons during development. These axons serve as information highways that enable neurons to communicate with different cells across the body. Among the many axons, only one needs to reach the target. The neuron prunes the rest.
Neurons rely on the same molecular machinery used during apoptosis for this process, but how they leverage apoptotic proteins while ensuring that they do not self-destruct or prune the wrong axon is unknown. Selena Romero, a graduate student in the lab of Mohanish Deshmukh at the University of North Carolina at Chapel Hill set out to better understand this mechanism.
She cultured peripheral neurons in the central section of a three-compartment microfluidic chamber. When Romero imaged the chamber for the first time, it took her five hours. She manually captured 100 images, overlapping each by approximately 20 percent, and then stitched them together using software.
The resulting image revealed fluorescently labeled neurons in blue growing through tiny grooves to enter the wider left and right compartments, where they produced axons, also in blue, that then began to spread. “I was blown away by how striking it was because the axons were not all on the left side or all on the right side. They branched out fully,” said Romero.
This proof-of-concept image demonstrated that Romero could use the chamber to model axon pruning by depleting a key protein needed for neuronal growth from axons in one compartment without killing the neuron or the axons in the other compartment. “That’s why these compartmentalized models are important for us because we can’t study pruning in any other context,” said Romero.
Laura is an assistant editor for The Scientist. She earned her PhD in biomedical sciences from Rush University by studying how circadian rhythms and alcohol affect the gut.
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Researchers developing antiviral therapies must race against rapidly mutating viruses.¹ Kent Kirshenbaum, a chemist at New York University, saw stable viral targets as potential solutions. So, he and his team targeted enveloped viruses because these viruses build their lipid membranes using host lipids. As a result, they do not change with mutations to viral nucleic acids.
In a recent study, Kirshenbaum and his team successfully damaged the membranes of different viruses using engineered peptide-like molecules. Their findings, reported in ACS Infectious Diseases, offer a novel antiviral targeting approach.²
Inspired by how the immune system combats pathogens with antimicrobial peptides, Kirshenbaum and his team turned to synthetic peptoids, which mimic the chemical structures and bioactivities of peptides. When designing and building peptoids, researchers can customize functional groups to increase their longevity in the body. Due to the unique properties of these molecules, Kirshenbaum described the peptoids’ broad-spectrum activity against bacteria, fungi, and viruses as pathogen agnostic.
Kirshenbaum’s team used three previously discovered linear peptoids and synthesized four new cyclic variations with increased antiviral activity for this study.³ When the team incubated the peptoids with infectious viruses, they effectively disrupted the membranes of all three enveloped viruses: Zika, Rift Valley fever, and chikungunya. Next, the team investigated which lipids were susceptible to peptoid-induced damage, and found that the peptoids specifically targeted phosphatidylserine.
“We've got a situation where molecules are targeting one component of a membrane that's displayed on the exterior of pathogens like viruses but is sequestered from the exterior of our human cell membranes,” Kirshenbaum explained.
Amelia Fuller, a chemist from Santa Clara University who was not involved in the study, said that these findings are a starting point for understanding how peptoids interact in the body as therapeutics. “You can easily swap in and out different [functional group] features to ask really specific and narrow questions about [peptoid] interactions in biological systems.”
For his next step, Kirshenbaum plans to look beyond viruses. By finetuning these peptoids, he hopes to explore their abilities to mimic other biologically active peptides for targeting other pathogens.
Danielle is an Assistant Editor at The Scientist. She has a background in neuroscience and molecular psychiatry. She has previously written for BioTechniques News, The Scientist, and Drug Discovery News.
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Wyss Institute at Harvard University
When T cells enter the tumor battlefield, they sense nearby antigens to inform their attacks. But chemical signaling isn’t the only property that shapes T cell function. In a recent paper, researchers described generating different T cell populations by altering mechanical properties of the cell environments.1 Their in vitro platform could improve production and customization of CAR T cell therapies.
“Different cell types just kill differently,” said Kwasi Adu-Berchie, a biomedical engineer at the Wyss Institute and coauthor of the paper. “We present other tools to generate some of these cell types.”
In vitro systems are valuable tools for studying and manipulating immune cell function, but many platforms only focus on chemical signaling. “We are adding a whole new layer of sensing and signaling based on the mechanical environment that further refines the signals that are presented by antigen presenting cells,” said Manish Butte, an immunologist at the University of California, Los Angeles, who was not involved in the study.
To study mechanical forces on T cells, Adu-Berchie’s team generated 3D hydrogels that mimic the tumor tissue environment. By modulating the density of collagen, they controlled the gel’s stiffness. The team also introduced crosslinkers into closely linked collagen fibers to independently tune viscoelasticity, which is the time-dependent recovery after deformation.3 For example, both a rubber band and silly putty are elastic, but the speed it takes for each item to bounce back after stretching differs.
When the researchers cultured T cells in these different hydrogels, they found that viscoelasticity strongly influenced T cell phenotypes. More elastic, less viscous gels produced effector-like T cells, which quickly mount an attack, while a less elastic, more viscous environment produced memory-like T cells, which contribute to long-term immunity. The researchers found increased activation of the transcription factor activator-protein-1 in the effector-like cells. They observed similar elevations in T cells extracted from patient tumors, which bear comparable viscoelastic properties.
The researchers hope to use these biomaterials to explore how mechanical cues shape other immune cells, such as natural killer cells and regulatory T cells.
Mariella is an assistant editor at The Scientist. She has a background in neuroscience, and her work has appeared in Drug Discovery News and Massive Science.
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Starting a lab is an exciting moment for an early career researcher, but it can also be overwhelming. Biological anthropologist Tina Lasisi recently started a new lab at the University of Michigan. Lasisi shared some of the lessons she learned while setting up her lab, where she will study the diversity of human hair and skin.
I was excited. During my graduate and postdoctoral training, I was involved in many lab management tasks such as mentoring students and hiring staff. Those experiences made this transition feel less like a big change and more like finally getting resources for what I was already doing.
I find the unwritten parts of the job the most challenging. When I first became a principal investigator, there was no manual to follow or list of milestones to accomplish. Since every lab is different, it's impossible for people in the institution to give one-size-fits-all advice. Ultimately, the most difficult thing about being independent is discerning what is important and needs to be done urgently from what just needs to be done eventually.
Put all the information from human resources, finance, or people in the new department in one place. Also, start thinking about hiring a team since this process can take a long time. Define who you want in your group. In some cases, it might make sense to have many graduate students, but in other cases, it might be better to have more postdoctoral researchers. Make sure to understand what each position means, how much it costs in terms of salary, and how much you can expect a person to do in the lab. Finally, think about team dynamics and how to build an environment that allows those people to succeed.
This interview has been condensed and edited for clarity.
Danielle is an Assistant Editor at The Scientist. She has a background in neuroscience and molecular psychiatry. She has previously written for BioTechniques News, The Scientist, and Drug Discovery News.
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New tools for tracking changes in gene expression with spatiotemporal precision could illuminate mechanisms driving development or disease progression. Inspired by the history recorded in tree rings, researchers developed a novel technology that records transcriptional events as fluorescent tags onto elongating protein chains embedded inside cells.1
Mariella is an assistant editor at The Scientist. She has a background in neuroscience, and her work has appeared in Drug Discovery News and Massive Science.
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Neuropeptides, small proteins released by the nervous system, regulate how much food animals eat. To find out how long these molecules have been playing this role, Vladimiros Thoma, an assistant professor at Tohoku University, turned his attention to jellyfish. “Jellyfish and also other animals called comb jellies are studied as candidates for the origins of neurons,” Thoma explained, making them perfect models for investigating that question.
Using the jellyfish Cladonema pacificum, Thoma and his colleagues discovered a peptide that controls feeding both in jellyfish and fruit flies, animals that shared a common ancestor millions of years ago. Their findings suggest deep evolutionary roots for the role of neuropeptides in appetite regulation.1
To identify the molecules regulating Cladonema’s appetite, the team starved jellyfish for about 50 hours and compared the gene expression profiles of starved and recently fed jellyfish. They found that feeding changed the expression of several genes, including those encoding neuropeptides. After screening the ability of these molecules to control food intake, they found five feeding suppressors, among which was the peptide GLWamide.
“These Wamide peptides were originally discovered in insects,” said Meet Zandawala, a neuropeptide researcher at the University of Nevada, Reno, who was not involved in the study. “It is quite interesting to find these peptides in such [simple] animals.”
The team also showed that GLWamide is expressed in neurons in jellyfish tentacles, and that it inhibited the tentacle contraction movement to suppress feeding.
Next, the researchers tested whether GLWamide worked similar to myoinhibitory peptide (MIP), a known appetite regulator in fruit flies. They bathed jellyfish with MIP and generated transgenic flies that expressed GLWamide but lacked MIP. They found that MIP reduced the jellyfish’s shrimp intake, while GLWamide decreased the number of times flies stuck out their proboscises to ingest a drop of sugary water.
“This signal is evolutionarily conserved. It is also in the flies, and it seems to work the same way,” Thoma explained. “It is kind of striking that over millions of years, you still have a very similar system.”
Zandawala believes that an important next step is to identify the target of the jellyfish peptide. Scientists may be able to investigate this and other questions as they develop tools to study this organism, Thoma said. “It has a bright future ahead.”
Mariella is an assistant editor at The Scientist. She has a background in neuroscience, and her work has appeared in Drug Discovery News and Massive Science.
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Trained as a computer scientist, Shantanu Singh now leads a data science team at the Broad Institute and applies a big data perspective to solve biology problems. He and his team are currently developing computational and statistical methods that allow researchers to mine microscopy images of cells and uncover patterns associated with diseases or chemical perturbations.1,2
I discovered the magic in mathematics and computing in middle school. I later combined this fascination with my interest in artificial intelligence and related fields to apply these concepts to solve problems in biology. I share the wonder of the physicist Eugene Wigner who believed that mathematical concepts could explain many phenomena. For me, it is not just about explaining our surroundings; rather, mathematics combined with computing can interpret the data that emerges from the natural world.
I joined Anne’s lab as a postdoctoral researcher because I wanted to work with someone who understood biology well. My relationship with her has evolved from mentorship to partnership, where I can brainstorm ideas, write grants, and mentor students. Our ability to empathize with one another, despite having very different backgrounds and work styles, helped us find that common language to build our research.
First, try to be bilingual. Don’t only understand computer science, for example, but also learn the basic aspects of biology. Enroll in a course to get past the initial hurdle of terminology because that is often the crippler. Second, it is important to realize that a nonbiologist will never be a biology expert, and that is fine. Science is a team effort, and finding someone who knows biology well is a great way to build a mutually beneficial relationship in which both can learn from each other.
This interview has been condensed and edited for clarity.
Stella Zawistowski is one of the fastest crossword solvers in America, with multiple top-ten finishes at the American Crossword Puzzle Tournament and a New York Times: Sunday personal record of...
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1. Group that includes earthworms and leeches 6. Foam on breaking waves 8. Evidence of past healing 9. Pure, as ethanol 10. Rubber produced by polymerization 12. Velocity, for one 13. Exhibit plasticity 16. Planet whose moons include Titan and Tethys 17. Brightest star in the constellation Lyra 18. Carnivorous mammal of the ocean 21. Fruits related to peaches and cherries 22. Amniotic membrane portion 23. Render unconscious 24. Quality of microswimmers
Film Eptfe Membrane 2. Organism's role within its ecosystem 3. Eustachian tube's location 4. Retinal projection 5. Nervous, immune, reproductive, and others 6. Abnormally hardened, as tissue 7. ___ cuff (shoulder muscle group) 11. Foot movement pattern in running or walking 14. Light-sensitive feature on a starfish 15. Tiny unit of electrical resistance 19. Fixture to which a DVD player might be connected 20. ___ stem cells 22. Unit of food energy, for short