Dr. Mike Lanz, who completed his Ph.D. at Cornell University before joining Stanford and the Chan Zuckerberg Biohub, has uncovered something unexpected: cell size is a fundamental determinant of protein composition. Through his work, he’s also come to learn about the value of better integrating biological and technical expertise and the importance of capturing the metadata that is often absent in most ‘omics’ datasets.

A chance encounter with mass spectrometry

Mike’s journey into proteomics began as a chance encounter with mass spectrometry during his Ph.D. rotations at Cornell University. Mike recalls an introductory lab demonstration, his first experience with mass specs. While Western blots, which he had done at that point, could measure one protein at a time, a single mass spectrometry experiment could measure thousands of proteins simultaneously. “I thought that was amazing. From then, I put myself around the instrument and found it to be useful in many other ways too.”

This early fascination led Mike down a path straddling the worlds of biology and proteomics. His Ph.D. work under Professor Marcus Smolka at Cornell focused on yeast genetics, phosphoproteomics, and DNA damage signalling, while the lab he joined during his postdoc was focused on how cell size is regulated by the cell division cycle.

“So that’s where it started. We ended up measuring cells of different sizes, and that that’s what my primary postdoctoral work has been: the main focus is proteome composition and how it reflects a cell’s size and growth rate.”

Big insights in cell size

His recent work has led to a surprising finding: as cells increase in size, their protein composition shifts in proportion to volume changes. “We weren’t expecting composition to change as a function of cell size. And it very clearly does,” says Mike.

The key factor isn’t size alone, but rather the ratio between a cell’s mass and its genome copy number. This finding has implications for multiple fields of biological research, from aging studies to cancer treatment evaluation.

“It changes everything,” Mike explains. “Many ageing models show cells getting bigger as they get older. If you’re studying age, how is that confounded by the fact that size is changing as age is changing?” The implications extend to cancer research, where common chemotherapy assays might need re-examination. When these genotoxic drugs prevent cells from dividing, the cells grow larger instead of multiplying. The changes previously attributed to drug treatments might actually be size-related effects.

“The fun part is that size is everywhere in biology, and now we know it it’s an important driver of change in physiology. Now the question is to disentangle what’s truly drug- or treatment- or age-specific, and what’s just the size effect?”

Read the full study here.

The importance of technology

Why did this fundamental aspect of cell biology go unnoticed for so long? To Mike, the answer lies in technological limitations. Previous studies relied heavily on microscopy, which could only examine one cellular component at a time. “But with the mass spec, you measure how everything changes relative to everything else. It’s a totally different perspective.”

Mike emphasises that they wouldn’t have come to these conclusions without proteomics. “In these experiments, the overall fold-changes are small. What I didn’t appreciate until I started doing a lot of these experiments is how good proteomics is at measuring widespread but subtle changes in a cell’s protein composition.”

This revelation highlights how advancing technology can reshape our understanding of biological systems. “You try not to get too caught up in what’s established science, because so much of it was established by people that don’t have the capabilities we have now.”

Capabilities like improvements in liquid chromatography. Mike highlights that his size findings may not have been possible without the amount of information he was able to extract from a single shot using Aurora Series columns. “The number of ion signatures that MaxQuant would identify as potential peptides was double that of our in-house column.”

Mike also emphasises his gratitude towards the Chan Zuckerberg Biohub, whose instruments he continues to use. “They’re the one that introduced me to the IonOpticks columns, and they paid my salary for several years. We would not have been able to make the progress that we made without that affiliation.”

…and the challenges of technology

At the same time, Mike cautions researchers against getting too focused on technological advancements alone. “The real bottleneck for advancing our understanding of biology is not technological. I think it’s people doing good experiments.”

Many people in the mass spectrometry field are analytical chemists or were trained in the art of mass spectrometry rather than biology. “The focus is far more on advancing the performance of the instruments than on what makes a good experiment.” To Mike, a more important question than “how many peptides can you identify in X amount of time” is “what are the biological experiments that need to be done right now?”

Thanks to his biology mindset combined with his hands-on mass spec experience, Mike uniquely bridges the technical and biological sides. However, he’s often seen the communication between biologists and those who acquire the data break down.

“So a biologist comes to a mass spec person and says, ‘I want to study this transcription factor’. ‘Ok, give me your sample. We’ll measure it’. But if someone didn’t stop and think ‘the mass spec might not be able to see this thing even if we measure 15,000 proteins’, you just wasted a ton of time. I think students that are training in molecular biology need to have a better understanding of how a proteomic measurement is made. The more that biologists interface with the technology, the less information that will be lost.”

How richer data could unlock future insights

Looking ahead, Mike sees exciting developments in single-cell and low-input proteomics. While still in its early stages, the acceleration of ultra-high sensitivity instruments allows for studies using far less material than before.

Mike sees equally exciting potential in a methodological, rather than technological, improvement: His future research aims to create more comprehensive datasets by combining protein composition measurements with parallel measurements of various physiological parameters, from growth rates to metabolic indicators.

“One of the things that’s lacking for a lot of not just proteomics data, but ‘omics’ data in general, is metadata. What I mean by that is parallel measurements of the things that you think are important for interpreting your omics data.”

Linking changes in these parallel measurements in the cell to changes that researchers observe in the proteome should help with interpretation. “Then, anytime you make a proteome measurement on some unknowns in a data set, you can make more concrete quantitative conclusions about that cell that you just measured,” Mike explains.

All this reinforces Mike’s earlier point that, while technological advancements open up new possibilities, they’re not the only avenue. We should also look to methodological improvements such as these to unlock new insights that advance the field.

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