##When did you first become interested in science, adn what was your journey to where you are today?

I grew up in Berkeley, surrounded by science from a young age-my mother was a mathematician, and my father a physicist. With both parents in academia, I was immersed in a scientific surroundings early on. Still, like many kids, I didn’t feel a strong connection to any one field at the time.

I wasn’t until college that I started gravitating toward science. I noticed that the courses I found most interesting always seemed to be in that realm. One thing I’ve always appreciated about science is its grounding in evidence.In the humanities, debates can go on endlessly, but in science, there’s often a definitive answer-that clarity really appealed to me.When it came time to choose a major, I landed on biochemistry. I was enjoying chemistry and found it engaging, so it felt like a natural fit. Later, somewhat unexpectedly, I realized I liked physics, a subject I’d initially avoided, perhaps because it was my father’s field. That led to an interesting situation: I was a biochemistry major who genuinely enjoyed physics.

At the same time, I was already doing research in the chemistry department, so I ended up with an interdisciplinary foundation. when I applied to graduate school, I chose biophysics to bring those threads together. I joined the biophysics program at Stanford, though I once again found myself working out of the chemistry department.

For my postdoc, I initially planned to focus on cell biology, a field I’d become interested in through earlier exposure.But plans shifted, and I ended up working on the growth of an automated DNA sequencer project that turned out to be incredibly rewarding. It brought together many of the skills I had picked up along the way: synthetic chemistry as an undergrad, along with fluorescence, optics, lasers, and electronics from grad school. All of it came into play and was crucial to the project’s success.

That project eventually opened the door to my first academic position, but the path there wasn’t easy. I spent two years on the job market. The first year was especially tough-neither I nor the hiring committees were quite sure how to define my expertise. I worked with DNA, so I figured I was a biochemist. And while the biochemistry department invited me to an interview, none offered a position.

Eventually, people started suggesting analytical chemistry, a field I hadn’t seriously considered. My only experience with it had been an undergrad class I didn’t find notably memorable.But during my job search, the analytical chemistry community, especially at the University of Wisconsin-was incredibly open and welcoming. They saw that I was tackling complex biological problems with a strong physical sciences background, and they appreciated that perspective. It turned out to be an excellent match, and that’s how I ended up as an analytical chemist at Wisconsin. 

##When did proteomics and proteoforms become part of your career?

I spent about 10 to 15 years focused on DNA sequencOur first tests applied this approach to the yeast proteome. Though, when we analyzed the data, we found we weren’t getting as many confident identifications as we had hoped. That’s when Mike Shortreed came up with a key insight in the lab. As he was looking at the data, he noticed that some unidentified masses were offset from known proteoforms by amounts corresponding to known post-translational modifications (PTMs).

If we had a proteoform with a confirmed identity and another molecule with a mass shifted by, say, the mass of a phosphorylation, we could reasonably infer that the second molecule was a modified version of the same protein. we began calling these Experimental-Theoretical (ET) pairs-a known proteoform paired with a related one predicted based on a theoretical mass shift.

Mike pushed this idea even further.He realized that even if we didn’t have a theoretical match for a proteoform, we could still detect relationships between experimental observations by looking at known PTM mass shifts.

These became our Experimental-Experimental (EE) pairs-molecules connected purely by observed mass differences. Using Cytoscape, a network visualization tool, we assembled these relationships into clusters we called proteoform families.

This approach significantly expanded the number of proteoforms we could connect and interpret. And conceptually, I’ve come to really appreciate it.It offers a more gene-centric view of proteomics. Traditionally, we say each gene makes a protein-but the definition of a “protein” is a bit fuzzy. Instead, we can think of each gene giving rise to a set of proteoforms-like a family of related molecules. Just as a family has parents, children, and cousins, a gene produces various forms of a protein through processes like alternative splicing or post-translational modification.This framework helps simplify how we think about biological complexity. I like to envision around 20,000 proteoform families-one for each gene in the human genome. Each family contains the different proteoforms derived from that gene.

If we want to truly understand biological systems, we need to measure how these families and their members respond to different conditions, environments, or perturbations.

Some members of these proteoform families have implications for diseases, including heart disease and COVID-19. Could you share some examples of how proteoforms are involved in these conditions?

A couple of examples come to mind. One is in cardiac biology. My colleague Ying Ge, who also works in top-down proteomics, has studied cardiac troponins-specifically troponin A.She’s shown that in diseased hearts compared to healthy ones, there are distinct differences in the phosphorylation states of these proteoforms.

That’s just scratching the surface,though. In biology-and science more broadly-there’s always the ongoing question of correlation versus causation.

One way to frame this is through the lens of biomarkers.If a specific phosphorylated proteoform can be consistently detected in blood and reliably indicates the presence of heart disease, it could serve as a diagnostic marker. Though, proving clinical utility takes time and rigorous validation.

The other possibility is that these proteoform differences are not just correlated with disease but actually causative. if that’s the case, then understanding the mechanisms that drive those changes could open up opportunit##What work is your lab currently doing to contribute to the development of these new technologies?

Most of our efforts to improve proteoform analysis right now are focused on the data analysis side. If you think about the typical workflow, we’re still operating squarely within the mass spectrometry framework. While I find nanopore sequencing engaging, I feel like I’m a bit late to that game-many groups are already deeply invested in that space. I haven’t yet come up with a new technology for proteoform-level analysis that sits outside of mass spectrometry.

So, within the mass spec world, I tend to think of the process in three parts: before the mass spectrometer, the instrument itself, and after the mass spectrometer.

Before using the instrument, you’ll need sample preparation and separation techniques.There’s definately room for advancement there, but most of the progress tends to be incremental. As for the instrument itself, these machines are incredibly complex. Companies like Thermo Fisher and Bruker have teams of brilliant engineers who are constantly pushing the boundaries of what the hardware can do.

But after the mass spectrometer? That’s where things get really interesting. The raw data that comes from these instruments is highly complex, and there’s still a huge amount of valuable data hidden in it. Extracting and interpreting that information is where a important portion of my group-about a third to half-is focused.

it’s a particularly exciting time to be working in this space, especially with the emergence of AI. If you think back, the Human Genome Project was powered in large part by advances in computing. In the 1980s, bioinformatics was still in its infancy compared to where it is now. I see the rise of AI as a similar inflection point. We’re already seeing its potential, but I believe we’re only beginning to understand how transformative it could be.

AI has the potential to unlock entirely new ways of analyzing proteoform data, which makes this moment so promising for the field.

##You mentioned trying to get the government’s attention for funding. What role does the private sector play in this field?

the private sector has shown strong interest in this space. Companies have correctly recognized that bottom-up proteomics already represents a large, well-established market, and they’re actively looking for ways to either take over or disrupt that space with new technologies.

A lot of these efforts are clearly inspired by what happened in DNA sequencing. Early on, the first human genome was sequenced using electrophoresis-based methods, but what really accelerated the field was the transition to next-generation sequencing (NGS). That leap involved innovations like combining array-based platforms with fluorescence-based sequencing-millions of sequencing reactions happening together on a chip, with high-resolution imaging capturing the results.

So now,it’s natural for people to ask: Can we do something similar for proteins? It’s not a far-fetched idea at all,and several groups are working toward that goal. Ed marcotte was one of the first researchers I saw exploring this space, though there may have been others before him. As then, a number of companies have entered the scene with similar concepts-trying to apply array-based, high-throughput strategies to proteomics.

The challenge for me, though, is that these technologies-at least in their current form-don’t capture proteoforms. They frequently enough focus on detecting peptides or protein presence but not the full molecular complexity of intact proteoforms, including post-translational modifications and sequen## Pittcon: A Global Hub for Laboratory Science Innovation

Pittcon stands as the preeminent annual gathering for the global laboratory science community, drawing over 16,000 professionals from diverse sectors including industry, academia, and governmental organizations, representing more than 90 nations . This expansive event serves as a crucial platform for showcasing the latest advancements in analytical chemistry, life sciences, and related fields.

### The Core Purpose: Advancing Scientific Endeavor

At its heart, Pittcon is dedicated to fostering the progress of scientific exploration. The institution’s primary objective is to support and perpetuate educational initiatives and philanthropic endeavors that directly benefit the scientific community. This commitment manifests in a variety of programs, including scholarships, awards, and funding for research projects.

### A Dynamic Ecosystem of Learning and Networking

Pittcon isn’t merely an exposition; it’s a vibrant ecosystem designed to facilitate knowledge exchange and professional development. The conference program features a complete array of technical sessions, workshops, and short courses, covering a broad spectrum of topics relevant to modern laboratory science.

recent trends highlighted at Pittcon include a surge in the application of artificial intelligence (AI) and machine learning (ML) to analytical workflows. According to a 2024 report by Grand View Research, the global AI in healthcare market is projected to reach $187.95 billion by 2030, with analytical chemistry playing a key role in data generation and interpretation . This integration of AI is streamlining processes, enhancing data accuracy, and accelerating discovery.

### Beyond the Conference Floor: Impact and Outreach

Pittcon’s influence extends far beyond the conference itself. The organization actively supports educational programs aimed at inspiring the next generation of scientists. For example, Pittcon provides funding for undergraduate research experiences and supports science education initiatives in local communities. This dedication to outreach ensures a continued pipeline of talent for the field.

Furthermore,Pittcon serves as a catalyst for innovation,driving the development and commercialization of cutting-edge technologies.The event provides a unique opportunity for researchers and industry leaders to collaborate, share ideas, and forge partnerships that accelerate scientific breakthroughs.

Proteoforms: Unlocking the Next Frontier in Protein Research

For decades, protein research has largely focused on understanding proteins as single entities defined by their gene sequence. However, this view oversimplifies the complex reality within our cells. Proteins rarely exist in a single, static form. Instead, they undergo a multitude of modifications, leading to diverse molecular forms known as proteoforms. These proteoforms, arising from genetic variations, alternative splicing, post-translational modifications (PTMs), and other processes, represent a meaningful paradigm shift in how we understand protein function and its role in health and disease. Recognizing and studying proteoforms is now unlocking the next frontier in protein research.

What are Proteoforms?

Think of a protein as a base model car. While the blueprint remains relatively constant, numerous customizations, such as paint color, engine upgrades, or interior features, can create highly distinct vehicles.Similarly, a single gene can produce multiple proteoforms through a variety of mechanisms. the human proteome, vastly more complex than the genome, reflects this proteoform diversity.

Key factors contributing to proteoform diversity include:

• Genetic Variations: Single nucleotide polymorphisms (SNPs) and other genetic variations can lead to amino acid substitutions, resulting in different protein variants (proteoforms).
• Alternative Splicing: Genes can be spliced in different ways, leading to different mRNA transcripts and, consequently, proteoforms with varying amino acid sequences.
• Post-Translational Modifications (PTMs): These are chemical modifications that occur after a protein is synthesized. Common PTMs include phosphorylation, glycosylation, acetylation, ubiquitination, and methylation. Each PTM can substantially alter a protein’s structure, function, and interactions.
• Proteolytic Cleavage: Proteins can be cleaved by proteases into smaller, functional fragments, each representing a distinct proteoform.
• Combinations: Often, multiple of the above modifications occur on a single protein molecule leading to a specific proteoform.

The Significance of Proteoform Diversity

Understanding proteoforms is crucial as they are the actual functional molecules in cells.The presence, absence, or abundance of specific proteoforms can have profound effects on cellular processes, signaling pathways, and disease development. Ignoring proteoform diversity limits our understanding of biological systems.

Here’s why proteoforms matter:

• Function: PTMs,for instance,can activate or inactivate a protein,alter its localization,or modulate its interactions with other molecules. A single protein can have multiple functions depending on its proteoform state.
• Regulation: The creation and degradation of proteoforms are tightly regulated. Dysregulation of these processes can contribute to disease.
• Disease: specific proteoforms can serve as biomarkers for disease, reflecting the molecular state of a cell or tissue. They can also be therapeutic targets.
• drug Response: Proteoform profiles can influence how individuals respond to medications. Understanding proteoform variations can definitely help personalize drug therapies.

Challenges in Proteoform Research

Studying proteoforms presents significant technical and analytical challenges. Traditional proteomics techniques frequently enough struggle to fully characterize the complexity of proteoform mixtures. The challenges mainly come from:

• Complexity: The sheer number of proteoforms in a cell or tissue can be overwhelming.

• Dynamic Range: Proteoforms exist in vastly different abundances, making it difficult to detect low-abundance species.

• Analytical Limitations: Separating and identifying proteoforms requires high-resolution separation techniques and sophisticated mass spectrometry methods.

• Data Analysis: Interpreting the complex data generated from proteoform analysis requires advanced bioinformatics tools.

technologies for Proteoform Analysis

Fortunately, advancements in technology are enabling researchers to tackle the challenges of proteoform analysis. These include:

• high-Resolution Mass Spectrometry (HRMS): HRMS allows for precise measurement of the mass-to-charge ratio of ions, enabling the identification of proteoforms based on their unique masses.
• Top-Down Proteomics: Unlike traditional “bottom-up” proteomics, which involves digesting proteins into peptides, top-down proteomics analyzes intact proteins, preserving facts about PTMs and other modifications.
• Liquid Chromatography (LC): LC is used to separate complex mixtures of proteins and peptides prior to mass spectrometry analysis. Newer LC techniques offer higher resolution and improved separation capabilities.
• Affinity Enrichment: Antibodies or other affinity reagents can be used to selectively enrich for specific proteoforms, simplifying the analysis of complex samples.
• Bioinformatics Tools: Sophisticated software tools are needed to process and interpret the large datasets generated from proteoform analysis.

Proteoforms and Personalized Medicine

One of the most promising applications of proteoform research is in personalized medicine. By analyzing a patient’s proteoform profile, clinicians can gain a deeper understanding of their disease state and tailor treatments accordingly. as an example:

• Cancer: Specific proteoforms can be used to diagnose cancer, predict prognosis, and monitor treatment response. Proteoform analysis can also help identify patients who are most likely to benefit from specific therapies.
• Neurological Disorders: Proteoform analysis can provide insights into the mechanisms underlying neurological disorders such as Alzheimer’s disease and Parkinson’s disease. Specific proteoforms can serve as biomarkers for these diseases.
• Cardiovascular Disease: Proteoforms can be used to assess cardiovascular risk and guide treatment decisions.
• Infectious Diseases: Proteoform analysis can help identify pathogens and monitor the host’s response to infection.

Case Studies: Proteoforms in Action

Let’s examine a few specific examples of how proteoform research is impacting our understanding of disease:

Case Study 1: Histone Modifications in Cancer

Histones, the proteins around which DNA is wrapped, are subject to a wide range of PTMs, including acetylation, methylation, and phosphorylation. These modifications, often referred to as the “histone code,” play a crucial role in regulating gene expression.Aberrant histone modification patterns are frequently observed in cancer cells. By studying histone proteoforms, researchers can identify potential therapeutic targets for epigenetic therapies.

Case Study 2: Tau Proteoforms in Alzheimer’s Disease

Tau is a protein that stabilizes microtubules in neurons. In Alzheimer’s disease, tau becomes hyperphosphorylated and misfolded, leading to the formation of neurofibrillary tangles. The specific phosphorylation sites on tau, and the resulting proteoforms, are highly relevant to disease progression.Analyzing tau proteoforms in cerebrospinal fluid can help diagnose Alzheimer’s disease at an early stage.

Case Study 3: Immunoglobulin Glycosylation in Autoimmune Diseases

The glycosylation patterns on antibodies (immunoglobulins) can significantly affect their effector functions. Altered glycosylation patterns have been observed in autoimmune diseases such as rheumatoid arthritis. Studying immunoglobulin glycoforms can provide insights into the pathogenesis of these diseases and identify potential therapeutic targets.

Practical Tips for Proteoform Research

Embarking on proteoform research can be daunting, but here are some practical tips to guide your journey:

• Start with a well-defined biological question: Clearly define the specific research question you want to address and how proteoform analysis can provide insights.
• Choose the right technology: Select the appropriate analytical techniques based on the complexity of your samples and the specific proteoforms you are interested in.
• Optimize sample readiness: Proper sample preparation is crucial for preserving proteoform integrity and ensuring accurate results.
• Use appropriate controls: Include appropriate controls to account for technical variability and ensure the reliability of your findings.
• Collaborate with experts: Proteoform research often requires expertise in multiple disciplines,so collaborate with experts in proteomics,mass spectrometry,bioinformatics,and biology.

The future of Proteoform Research

The field of proteoform research is rapidly evolving. As technology advances, we can expect even more sophisticated tools and techniques to emerge, enabling us to study proteoforms with greater precision and detail. Future directions in this field include:

• Developing more sensitive and high-throughput proteoform analysis methods.
• Creating complete proteoform maps for different cell types and tissues.
• Integrating proteoform data with other omics data (genomics, transcriptomics, metabolomics) to gain a more holistic understanding of biological systems.
• Developing new proteoform-targeted therapies for a wide range of diseases.

Proteoforms: benefits and Advantages

The shift toward understanding proteoforms offers significant benefits in several areas:

• Improved disease diagnostics and prognostics
• More effective and personalized therapies
• A deeper understanding of basic biological processes
• Identification of novel drug targets
• Enhanced drug development strategies

Here is a table summarizing different PTMs and their effects:

Proteoform Data Analysis: A Practical Guide

Analyzing proteoform data can be challenging due to its complexity. Here’s a simplified step-by-step guide to navigating the process:

1. Raw Data Processing: Begin with raw data files from mass spectrometry or other analytical techniques.
2. Proteoform Identification: Use specialized software to identify and quantify individual proteoforms based on their unique characteristics.
3. Statistical Analysis: Apply statistical methods to identify significant differences in proteoform abundance between different conditions or groups.
4. Biological Interpretation: Integrate your findings with existing biological knowledge to understand the functional implications of the observed proteoform changes.
5. Visualization: Create informative visualizations, such as heatmaps or pathway diagrams, to communicate your results effectively.

The role of Bioinformatics in Proteoform Discovery

Bioinformatics plays a pivotal role in managing and interpreting the massive datasets generated in proteoform research. Here’s why it’s essential:

• Data Storage and Management: Efficiently storing and managing large proteomic datasets requires specialized databases and infrastructure.
• Algorithm Development: Advanced algorithms are needed to identify, quantify, and annotate proteoforms accurately.
• Pathway Analysis: Bioinformatics tools can map proteoforms to specific biological pathways and processes.
• Predictive Modeling: Machine learning techniques can be used to predict the functional consequences of proteoform variations.