General

Timing Matters in Legionella Testing

Posted on by Parker Smith

Several recent Legionella outbreaks have occurred around the globe. In New York, seven people died and 92 tested positive for Legionnaires disease (prompting improved testing procedures approved by the Governor). In Basel, Switzerland, one person died and twenty-eight contracted Legionnaire’s disease. Even in Linz, Austria, a recent outbreak has led to 20 infected.

What are the key differences between the New York and Switzerland outbreaks: the response time. In New York, operators had to rely solely on slower culture-based methods to identify the outbreak source. In Basel, while operators were able to use qPCR to quickly identify the likely outbreak source, while they also performed culture-based tests to confirm the qPCR results. This enabled operators to identify the contaminated cooling tower and shut it down preventively while awaiting formal regulatory confirmation. The faster source identification with qPCR was a key factor in response speed.

So, what can companies and governments do? They should test more frequently for Legionella pneumophila using faster methods like quantitative polymerase chain reaction (qPCR) and viability qPCR for cleaning verification as part of their operational procedure. Regular testing makes infrastructure safer for everyone and proactively reduces risk for companies.

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Internships at Promega: What Did You Learn?

Posted on by Promega

Each summer, nearly every department at Promega welcomes interns onto their teams. In only three months, these interns make critical contributions to the business and leave with valuable experiences to complement their education.

Four interns shared thoughts on their time at Promega, and how their accomplishments connect to their educational journeys.

Brady Anderson, Scientific Applications Intern, shares his work applying PCR tools to agricultural testing.

Eva Eckel, Environmental Health & Safety (EH&S) Intern, connects three different rotations to her coursework in environmental science.

Ethan Hazra, Process Engineer Co-Op, recalls the rewarding challenge of repairing equipment in an environmental control room.

Ashley Leighton, Corporate Hospitality Intern, describes how she helped her team improve their metrics analysis.

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From a Dying Reef in French Polynesia to a Global Restoration Mission: Meet the Coral Gardeners

Posted on by Alena Nickolenko

Titouan Bernicot founded Coral Gardeners, a nonprofit driving coral reef restoration, at just 18 years old. He was born on a pearl farm on a small atoll, a ring-shaped coral island, in French Polynesia. Instead of playing in the dirt with neighborhood friends after school, Bernicot, the only kid on the little atoll, spent his childhood in the water, immersed in the colorful and flourishing life around him (1). With no shops or markets, the community sustained themselves only with what the island had to offer, therefore relying on a thriving local ecosystem. “A really connected-to-nature way of living” says Bernicot (2). Largely disconnected from the rest of the human world but uniquely integrated with the natural one.

Photo Credit: Coral Gardeners

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Under Pressure: The Nuclear Choreography upon Replicative Stress

Posted on by Promega

Today’s blog is guest-written by Jana Krietsch (University of Zurich), edited by Nour Mozaffari (Promega)

Each time a cell divides, it must make an accurate copy of its entire genome—in human cells, this means roughly 6.2 billion individual DNA building blocks to duplicate. This enormous molecular task takes place inside the nucleus, a crowded, highly organized, yet remarkably dynamic environment. As the DNA-copying machinery moves along the genome, it may encounter roadblocks such as damaged DNA, sequences that are difficult to copy, tightly packed chromatin, or other molecular processes using the same DNA at the same time. These obstacles can slow or stall replication, a phenomenon known as DNA replication stress.

A Hidden Vulnerability of Cancer Cells

Cells are well equipped to deal with replication stress. Depending on the type and severity of the problem, they activate specialized signaling and repair mechanisms that protect replicating DNA. Failure of these responses can jeopardize genome integrity and result in permanent genetic changes that contribute to disease development.

In cancer, oncogenic changes and rapid proliferation places tumor cells under persistent replication stress. This promotes genome instability, a cancer hallmark, and drives tumor evolution. At the same time, it makes cancer cells hyper-dependent on replication stress response mechanisms to survive, creating a hidden vulnerability. Several treatments exploit this Achilles’ heel by increasing replication stress levels beyond what cancer cells can tolerate.

From Sequential Model to Dynamic Choreography

Many stress response pathways have been successfully reconstituted in the test tube. They are typically described as linear sequence of events triggered when an active replication site, termed replication fork, encounters a roadblock: the fork encounters an obstacle, its structure changes, signaling proteins are activated, repair factors are recruited, and DNA synthesis eventually resumes.

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ADC Development: The Questions Running in the Background

Posted on by Elise Johnson

You’ve had food stuck in your teeth at some point during a conversation you thought was going well. Or toilet paper trailing from the back of your shoe on a day you felt particularly put together. Maybe you’ve even forgotten to color in your very blonde eyebrows and spent the rest of the day looking like someone erased the top half of your face. But you had no idea. You’re walking around with the quiet, complete confidence of someone operating on incomplete information.

You weren’t wrong about anything you could see. You just couldn’t see everything, which is a different problem than getting a bad result. A bad result tells you something is wrong, but this doesn’t. This happens in the lab too. The number is clean, the program is advancing, and somewhere in the data, something is happening that your readout has no way to show you.

Antibody-drug conjugate (ADC) development has a version of this problem. The cytotoxicity readout is real, reliable, and correct. It’s also an aggregate, and an aggregate compresses everything that happened into a single number. That number can’t tell you which mechanisms produced it, which ones are underperforming, or what to change if the program stops working. It just tells you cells died, or they didn’t. You’re walking around with the quiet, complete confidence of someone operating on incomplete information.

Cytotoxicity: Where Every Program Begins

Cell viability assays were built to answer one question: did the cells die? Increase the dose, more cells die. Decrease it, fewer do. The curve is clean, the data is reliable, and the payload is doing what it was designed to do. For cytotoxicity, it’s the right question to ask.

That question has an established platform with consistent, reproducible data: our CellTiter-Glo® and RealTime-Glo™ Assays.

For most ADC programs, this is where the measurement work starts and stops, but it should only be where it starts. The question they answer well is only one of several your ADC is raising. The number means what it says, but the question it answers has a boundary, and the boundary leaves you with an incomplete picture.

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Time Capsules in the Language of Science

Posted on by Elise Johnson

In elementary school, I spent an art class layering greens. Jungle green fabric with a subtle coordinating stripe, delicate spring green tissue paper so translucent you could see through it, pieces of chunky emerald felt cut into the shape of leaves, sparkling seafoam and teal glitter, all of it pressed and glued onto a piece of tagboard in what my teacher called a monochromatic collage. All shades of one color. Mono: one. Chroma: color. I didn’t think about the word at the time because I was too busy glittering.

That same week, I took a science quiz on monocots and dicots and got the answer wrong. I couldn’t remember which plants had one seed leaf and which had two. When I got the test back and saw my error, it clicked. Of course a monocot has one seed leaf. I’d been using mono in art class while missing it on a science quiz down the hall. The root moved between rooms, but I hadn’t learned yet that it could.

That stuck with me. Although my formal education is in English and linguistics, my mind naturally sought out scientific connections. Greek and Latin roots were the place where those two interests didn’t have to compete. They had the precision of formulas, a combinatorial logic where twenty roots could unlock hundreds of words, and they worked on both sides of the hallway.

I write about science now, and the roots have remained my constant companion. A few weeks ago I was researching an organism called Rapaza viridis for a blog post on endosymbiosis. I’d never heard of it, but since viridis is Latin for green (the same root that gives us “verdant”), I already guessed this creature had something to do with photosynthesis. It does. R. viridis is a single-celled predator that steals chloroplasts from the algae it hunts and uses them to photosynthesize.

Learning more about the organism, I came across the term “transient chimerism.” Also new to me, but also immediately legible. The trans in “transient” is the same as in “transparent” and “transport”: ‘across,’ ‘through.’ Something passing through. Chimera: the Greek monster stitched together from a lion, a goat, and a serpent. Put together: a temporary state of being made from parts of more than one organism. The roots came through for me once again.

This keeps happening. I’ll hit a term I’ve never seen, and its pieces already feel familiar. I could spend this entire blog walking through terms and showing you their roots, but definitions only stay interesting for so long (and my family already compares me to the father in My Big Fat Greek Wedding). What I’ve become more interested in is something the roots do beyond defining. The roots that end up in a name tend to carry more than a definition. They carry an interpretation, an argument about the thing itself.

For years, I wondered about apoptosis. I knew apo meant ‘away from.’ I could see ptosis maybe shared something with “asymptote,” but I couldn’t figure out what cell death had to do with calculus. In 1972, John Kerr, an Australian pathologist, had been studying a form of cell death that looked nothing like anything he’d seen before. For most of pathology’s history, the only cell death anyone studied was the kind that showed up when something had gone wrong: a cell damaged by injury or infection or toxins, dying violently. That kind of death had a name: necrosis, from the Greek nekros (‘corpse’). The cell swells, ruptures, spills its contents, and triggers inflammation. Studying liver tissue, Kerr noticed a second kind of death happening quietly alongside it, in cells that hadn’t been damaged at all. The cell death he was watching was the opposite. The cell shrank, and its contents condensed. It broke apart into tidy packages that neighboring cells quietly absorbed. No mess. The body had planned this.

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Mitigating Disease by Testing Wastewater at Large Events

Posted on by Parker Smith

The FIFA World Cup has drawn fans from dozens of countries around the world to the United States, making it one of the largest international sporting gatherings in history. With thousands of people gathering together, it’s important for scientists and public health professionals to track contagious diseases. These pathogens spread easily, which is why early detection matters. 

Testing everyone who comes into an event is impractical, and if you wait too long, the disease may have already spread. How then do scientists preemptively use wastewater to check for disease spread without testing every individual?  

How do scientists test for wastewater? 

Wastewater surveillance, or wastewater-based epidemiology (WBE), is a rapidly growing field that has recently proved effective in tracking the spread of diseases in communities around the world. WBE refers to the process of analyzing the wastewater output from a population to detect the presence of certain compounds or pathogens. Though its use became widespread during the pandemic, it continues to show utility in monitoring other infectious diseases as well, including polio, influenza and monkeypox, among others.  

Samples collected from wastewater treatment plants provide a wealth of information, but the output from individual buildings can sometimes offer more specificity as to where exactly a pathogen is circulating. Occasionally, viruses that go undetected in samples from a treatment plant are still found on a micro level in sewage from facilities like hospitals or schools. 

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AI Identifies the Target. Someone Still Has to Validate It.

Posted on by Elise Johnson

Did you see the movie where Spider-Man files his taxes? Or the one where Wonder Woman sits on hold with her insurance company while her pasta water boils over? Or where Captain America finds blight on his tomato plants and drives to the county extension office where he spends fifty minutes with a seventy-four-year-old master gardener named Marlys then leaves with a handwritten note covering his soil composition, his watering schedule, and what Marlys calls “the mulching situation”?

No. Because the ordinary day-to-day doesn’t stand a chance next to the saving of the world.

We spend most of our lives in the ordinary. Not because we’re failing to reach the extraordinary, but because the ordinary is what holds everything together while we get there. It’s not the backdrop but the foundation. It’s what the story depends on, whether or not it gets any credit.

Drug Discovery Has a Storytelling Problem

Drug discovery runs almost entirely on ordinary days, punctuated by the moments that make the news: a new target gets identified, a compound shows promise, a trial produces results. Those moments get the headlines, press releases and keynote slots. What doesn’t get the same attention is the years of work behind those moments: the assays, the failed experiments, the redesigns, the slow accumulation of evidence that either holds up or doesn’t. That work has always been the majority of drug discovery.

Some of the most important work in drug discovery ends in a result nobody publishes, but a dead end isn’t a failure of the program. It’s the program working. The researcher who rules something out has learned something true. That knowledge travels forward even when it doesn’t make the headline because it can redirect the next hypothesis, narrow the next experiment or just quietly move things along. That work moves research forward without anyone announcing it.

The Shiniest Thing in the Room

Artificial intelligence is drug discovery’s latest extraordinary announcement, and the fanfare is legitimate. Most of the druggable proteome has never been touched. Of approximately 4,500 human proteins considered druggable, all approved drugs to date work through only 716 distinct targets. Drug hunters knew there was more biology to address but lacked a way to find and prioritize candidates at scale. AI is changing that. By scanning genetic evidence, biological networks and scientific literature at a scale no human team can match, AI is surfacing targets that were previously out of reach and ranking them by the strength of the evidence behind them.

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Another Crack in the RAS Code: The Measurement Story Behind Daraxonrasib

Posted on by Elise Johnson

In late May 2026, a clinical trial result landed in the New England Journal of Medicine and immediately rewrote what oncologists believed was possible for patients with metastatic pancreatic cancer. Before the paper was published, people in the field were already calling it “transformative.” The data, when it came, agreed. In a disease where most second-line treatments offer months at best, a drug called daraxonrasib nearly doubled how long patients lived compared to those who received chemotherapy.¹

RAS proteins, which regulate cell growth and are mutated in more than a third of all human cancers,² had spent forty years resisting every attempt to drug them. The protein’s surface offered no obvious foothold for a small molecule. Once the word “undruggable” attached itself to the RAS protein family, most of the field moved on to more cooperative targets.

Some researchers stayed. And Promega stayed committed to the question that never goes away: does this new compound work inside a living cell? When the next chapter of the RAS story arrived, the tools were ready. Daraxonrasib is one culmination of a much longer story, one that matters for every researcher pursuing a target the field has written off.

The First Answer

To understand what daraxonrasib represents, it helps to see how an earlier chapter of the RAS story faced the same fundamental measurement challenge.

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Measuring What Matters in Metabolic Research: A Bioluminescent Toolkit

Posted on by Promega

This guest blog post is written by Aisosa Omere, Product Marketing Intern at Promega.

Metabolic diseases fundamentally arise from disrupted cellular communication. In type 2 diabetes, cellular responsiveness to insulin is impaired. Within cancer, tumors alter their metabolic pathways to gain a proliferative advantage. In both conditions, dysfunction extends beyond individual molecules or pathways and involves a complex, interconnected network of metabolites, enzymes, and signaling molecules that dynamically respond to environmental changes. Traditional approaches to studying these networks often required a compromise: stopping experiments, lysing cells, and analyzing the resulting components. Although effective, this method is inherently limited. It captures a snapshot of what was present, rather than how the biology was actually behaving.

That compromise is becoming less necessary. The evolution of bioluminescent tools is changing what is possible. Some allow researchers to watch protein behavior and drug engagement directly in living cells in real time. Others offer faster, more sensitive detection of metabolites at physiologically relevant concentrations, and are compatible enough to run multiple assays from the same experiment, making coordinated, multi-pathway profiling practical in a standard lab setting.

An analysis of eighteen peer-reviewed publications from 2025 and 2026 shows just how quickly these approaches are taking hold across metabolic disease research. What follows explores the tools making this possible and why this shift represents one of the most consequential methodological changes in metabolic disease research in recent years.

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