The Hidden Costs of “Good Enough” Extraction

The translational oncology landscape has changed dramatically in the past two decades, and with it, the demands on the laboratories doing this work. Today’s translational oncology workflows require DNA and RNA from the same FFPE tissue section, cell-free DNA from large plasma volumes, and nucleic acids from heterogeneous batches of sample types processed in a single run. The analyte diversity has increased dramatically, and at the same time, the downstream assays interrogating those samples have grown more sensitive. The operational pressures have grown alongside the scientific ones. Labs are processing more samples than ever, but not with proportionally more staff. Same-day extraction to analysis is increasingly the expectation, not the exception. All of that change and complexity lands at the extraction step first.

Scientist holding up an FFPE tissue sample. Such samples are vital for nucleic acid extraction required for retroactive studies.

Extraction has long been treated as the step before the experiment, the part you complete before the real work begins. However, as these pressures on the translational laboratory grow, overlooking potential issues with extraction could be disastrous, particularly for labs working with limited, irreplaceable samples, because pre-analytical variability at the extraction step propagates through every downstream process. When extraction is overlooked, information in a sample can be lost and with it the insight into the biological question your downstream assay is asking.

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The Hidden Switch That Controls Lysosome Function

This image was generated by AI.

Your cells are constantly juggling two opposing needs: breaking things down and building things up. At the heart of that balancing act are lysosomes—tiny, acid-filled compartments that digest worn-out proteins, recycle cellular debris, and help cells decide whether it’s time to grow or conserve energy.

When lysosomes malfunction, the consequences can be serious. Lysosomal storage diseases, neurodegeneration, and metabolic disorders have all been linked to disrupted lysosome function. A new study published in Nature Communications has uncovered a key part of the control system that keeps lysosomes functioning properly.

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Promega Builds Opportunity to Appreciate Hidden Wetland

Nevin Springs now features a 720-foot boardwalk constructed by Promega.

There’s a turkey in the middle of the boardwalk. A large female, she pauses for a while and surveys the wetland around her. Then, with two steps and a hop, she disappears into the tall grasses.

A rose-breasted grosbeak sings from the treeline. Warblers chase each other above the dry remnants of last year’s cattails. And nearby, the namesake artesian springs bubble from the earth as they have year-round, unseen by almost anyone, since before the recorded history of the land begins.

This protected parcel of wetland, called Nevin Springs, plays an important role in the local ecosystem and broader watershed. Now, thanks to a boardwalk built by Promega Corporation, these hidden springs can be accessed and appreciated for the first time.

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The Molecular Blueprint for Virus-Resistant Cowpea

Cowpea (Vigna unguiculata), a humble tan and black legume, is one of the most important food crops in the world. Grown across sub-Saharan Africa, Asia, and parts of the Americas, Cowpea provides protein-rich nutrition for hundreds of millions of people, making it a cornerstone of smallholder agriculture. But cowpea production faces a persistent threat: the cowpea aphid-borne mosaic virus (CABMV), a common virus that can devastate yields across entire growing regions.

A dark background with a wooden spoon holding tan and black beans scattered on the spoon and on the background.

What makes CABMV particularly difficult to combat is how the virus infects its host. Instead of relying on viral translational machinery, the virus hijacks the plant’s systems to replicate. CABMV targets a protein called eIF4E, a translation initiation factor that the plant needs to read its own genetic instructions and produce proteins. The virus produces a protein, VPg, that binds directly to eIF4E and redirects the plant’s translational machinery to produce viral proteins instead. The plant can’t simply get rid of eIF4E. Without it, protein synthesis stalls. So how can cowpea defend itself against a virus that exploits one of its most essential proteins?

A new study published in Agronomy by researchers at the Federal University of Pernambuco, the Federal University of Minas Gerais, and Embrapa Recursos Genéticos e Biotecnologia takes a comprehensive look at this problem from the inside out1. The team characterized all three members of the eIF4E gene family in cowpea  (eIF4E, eIF(iso)4E, and nCBP) across six cultivated varieties (cultivars) with known contrasting responses to CABMV infection. Two of those cultivars (Bajão and IT85F-2687) are resistant to the virus; the other four (Boca Negra, BR14 Mulato, Pingo de Ouro, and Santo Inácio) are susceptible to the virus.

Using a multi-omics approach that combined genomic, evolutionary and structural analyses, the researchers set out to answer a fundamental question: what makes some versions of eIF4E exploitable by the virus, and others not?

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Measuring What Matters in Metabolic Research: A Bioluminescent Toolkit

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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Advancing Canine Cancer Immunotherapy through One Health Comparative Oncology Research

The Case for Canine Immunotherapy

Cancer does not respect species boundaries. Each year more than four million dogs are diagnosed with cancer (1), making it the leading disease related cause of death in the canine population. Osteosarcoma, lymphoma, mast cell tumors and mammary carcinomas are among the most prevalent (1). In many cases, these tumors in dogs bear striking biological and molecular similarities to their human counterparts.

This convergence is the foundation of the Comparative Oncology (2) framework and One Health Initiatives. Companion pets, like dogs and cats, share our environments, our lifestyles and increasingly our therapeutic challenges. When research advances in veterinary oncology, it can open windows into human disease as well.

Comparative oncology integrates the advances and research of veterinary science. Dog and cat against a background of antibodies.
Comparative oncology integrates the advances and research of veterinary science, especially those of companion animals like dogs and cats, into more general oncology research, advancing the entire field of oncology.

What Veterinary Checkpoint Immunotherapy Brings to Comparative Oncology

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Screening Disease-Relevant Biology: How the Endo-GeneScreen Platform Uses Endogenous Protein Detection to Drive Drug Discovery 

Drug discovery has long grappled with a fundamental tension in high-throughput screening: the more biologically relevant your model, the harder it is to scale. Phenotypic assays in primary disease-relevant cells offer rich biological context, but capturing meaningful, target-specific readouts from these complex systems at screening scale has remained a significant challenge.  In contrast, simpler, more scalable systems are easier to deploy but sacrifice the biological fidelity that makes hits meaningful. A recent study by Samowitz et al. in Nature Communications describes an interesting approach to resolve this tension, the Endo-GeneScreen (EGS) platform. A high-throughput screening system designed to enable scalable detection of endogenous protein levels within disease-modeling cellular contexts. 

A Well-Chosen Proof of Concept 

The authors selected Syngap1 as their proof-of-concept target to develop and demonstrate this approach. De novo mutations in this gene that lead to haploinsufficiency are among the most common genetic causes of sporadic neurodevelopmental disorders, including intellectual disability, autism, and epilepsy. Small molecules that boost SynGAP protein levels back toward wildtype would address the root cause of these disorders rather than managing downstream symptoms. Importantly, Syngap1 function is closely tied to cortical excitatory neurons. Well-validated in vitro and in vivo models for these neurons already exist, creating an integrated system for both discovering new compounds and validating them in the same biological context. That continuity is an important step toward improving the translational relevance of lead molecules coming out of the screen. 

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Tiny Edits, Big Harvests: How a Massive Mutation Screen Could Transform Crop Engineering

What if we could boost crop yields—not by adding foreign genes, but by tweaking the plant’s own DNA in just the right places?

For decades, plant scientists have known that the noncoding DNA flanking a gene—its promoter and regulatory regions—acts like a volume dial, controlling how much protein the gene produces. Adjusting that dial is the premise behind an approach called quantitative trait engineering (QTE), where CRISPR is used to make small, precise changes to these regulatory sequences instead of inserting entire transgenes. The appeal is enormous: nontransgenic edits face fewer regulatory hurdles and are more likely to gain public acceptance.

The problem? We don’t really understand the rules governing plant promoter architecture. Which nucleotides matter? Where can you cut, insert, or swap bases to crank expression up—or dial it down? Previous attempts to answer these questions have been limited in scale and have rarely uncovered gain-of-function mutations that increase gene expression. Now, however, a new study published in Nature Biotechnology suggests we’re closer to that reality than ever before.

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Charged Molecular Glues: The Molecular Trojan Horse That Slipped Past Biology’s Toughest Bouncer 

The cell membrane is notoriously selective about what it lets in. Charged molecules? Mostly rejected at the door. That’s a problem, because some of the most promising drug targets sit behind that barrier, and reaching them requires chemistry the membrane won’t tolerate. 

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What Counts as Evidence

This is the third post of three in a series leading up to the 16th annual International Forum on Consciousness, taking place in Madison this May. Hosted by the BTC Institute, Promega and Usona Institute, the forum gathers scientists, philosophers, and practitioners from dozens of different fields to investigate the nature of the mind. This year’s theme, “Unspoken Intelligence,” explores forms of perception and knowing that fall outside conventional cognition.

In 1845, mathematician Urbain Le Verrier calculated where an unseen planet had to be based on irregularities in Uranus’s orbit, wrote a letter to an observatory telling them where to point their telescope, and Neptune was there. He found a planet without ever looking up.

This is what third-person inquiry looks like at its best: observe from the outside, measure what anyone with the right instruments can measure, build a model precise enough to predict what no one has seen yet. Then look. The history of science is full of such moments, equations pointing to phenomena that hadn’t been detected, particles that hadn’t been observed, forces that hadn’t been measured. The method works because it is ruthlessly disciplined about what counts as evidence. The observer is removed, the conditions controlled, and the measurement trusted.

That discipline is not a limitation. It is the engine of over four centuries of extraordinary results. It gave us germ theory, the structure of DNA, and the sequenced human genome. Every time something seemed to resist physical explanation, the method eventually found the mechanism and the method held. The winning streak was long enough that the assumption underneath it stopped looking like an assumption. Outside-in, third-person, measurable evidence stopped looking like one way of knowing. It started looking like the definition of knowing itself.

The assumption felt safe because it had earned its confidence. Digestion, heredity, mental illness, each had seemed to resist physical explanation until it didn’t. The pattern was consistent enough that the method felt inevitable rather than chosen.

Then science turned toward consciousness, and the winning streak entered dangerous territory.


Here is the problem, what philosopher David Chalmers named the “hard problem” of consciousness in 1995.

To understand what Chalmers meant, it helps to start with his own illustration. When you see red, something measurable happens. Light hits the retina. Signals travel along the optic nerve. Specific regions of the visual cortex activate in patterns that neuroscientists can map with increasing precision. All of that is, in principle, fully describable by the third-person, outside-in approach. Given enough time and instruments, you can trace the whole sequence.

What you cannot describe from the outside is what red looks like. The redness of red, that specific quality of experience that exists only in the moment of seeing it, is not in the neural map. No better scanner will find it there, because the felt quality of the experience isn’t a physical thing hiding in the data. It exists only from the inside. The outside measurement, however precise, cannot reach it.

Chalmers used “hard” deliberately, in contrast to what he called the “easy problems” of consciousness: how the brain integrates information, focuses attention, produces behavior. Those are genuinely difficult, but the outside-in approach knows how to go after them. The hard problem is different in kind. It’s the question that remains even after you’ve solved all of the “easy” ones: why does any of it feel like anything at all?

Think of it this way: everything the brain does could, in principle, happen without any felt experience attached. Processing, responding, behaving, all of it could run like a machine in the dark, with no one home. The question Chalmers is asking is why it doesn’t. Philosophers ask it this way: why is there something it’s like to be you, right now, reading this?

No amount of outside-in evidence, however precise, touches that question, not because the science is insufficient but because the method was specifically designed to exclude first-person data. That exclusion was the whole point. It’s what made the outside-in approach so powerful everywhere else.

With consciousness, the method’s central design decision runs into a question it wasn’t built to answer: how do you study first-person experience when your method was built to exclude first-person data?

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