Automating Mass Spectrometry Workflows: Improving Data Quality, Integration, and Decision-Making
Automation reduces peptide mapping validation time while improving data usability.
As mass spectrometry (MS) technologies continue to scale in throughput and resolution, researchers are generating unprecedented volumes of data.
In research and development, the bottleneck has shifted: extracting reliable, actionable insights from this growing data burden.
Technology Networks spoke with Aude Tartière, the head of Expressionist at Genedata, who has been instrumental in advancing enterprise-grade MS data workflows.
In this interview from the 74th ASMS Conference on Mass Spectrometry and Allied Topics, Tartière shared how automation, data integration, and AI are reshaping biotherapeutics characterization and quality monitoring, streamlining and automating workflows, and accelerating decision-making across drug discovery and biopharma.
Flexible workflow automation and peptide mapping improvements in Expressionist
What are you bringing to researchers?
Expressionist has historically been recognized for its flexibility in MS data analysis, particularly among expert users. Rather than offering rigid, predefined pipelines, the platform enables scientists to design custom workflows tailored to specific experimental needs.
“You can essentially build the method that you really need for your data, but the added value of Expressionist is that when you know this method is ready, it can be saved and shared with other people within your organization,” said Tartière.
This approach has allowed organizations to codify expert knowledge into reusable workflows, reducing dependency on specialist users and enabling standardization across teams, while supporting all types of new modalities out of the box—from unnatural amino acids to the most complex biotherapeutic constructs.
A focus of the latest 2026.1 release was extending this capability through enhanced automation, especially in peptide mapping—an essential workflow in biotherapeutics characterization.
Peptide mapping involves enzymatically digesting proteins and analyzing the resulting peptides via MS to confirm sequence integrity, post-translational modifications, and product quality. However, one of its most time-consuming steps is validating peak integration and annotation.
“The main bottleneck is around verifying the data; often, scientists have to spend a lot of time doing that, and if they don't have the expertise, they might not do it properly,” explained Tartière.
To address this, Expressionist introduced:
- Automated peak integration adjustment tools and interactive adjustment tools
- Embedded expert logic within algorithms to automate the process
- Flagging systems to prioritize results requiring review
These features aim to reduce the burden on specialists while maintaining scientific oversight. The system does not remove the need for human validation, but accelerates it significantly.
Alongside these updates, improvements in deconvolution algorithms enhance spectral clarity, while expanded reporting capabilities ensure outputs can be readily used in downstream systems such as molecule workflow systems (e.g., Genedata Biologics) or electronic laboratory notebooks (ELNs).
MS workflow automation and peptide mapping benefits:
- Flexible workflows allow organizations to standardize and scale MS expertise
- Peptide mapping validation bottleneck is reduced by orders of magnitude
- Automation combined with embedded logic reduces manual workload
- Enhanced reporting supports downstream usability and compliance
Why data integration remains a challenge in mass spectrometry
Why is data integration still such a difficult problem to solve?
Despite advances in MS technologies, data integration continues to present significant challenges, particularly in enterprise environments where multiple platforms must interact seamlessly.
Tartière highlighted that many legacy MS tools are limited by inflexible, “black box” approaches: “Mass spec software typically has methods that are kind of like a black box. They have a method you can use. But if you don't like it, you're kind of stuck, and then the results are usually exposed in a certain format. Moreover, most MS tools are isolated on a scientist's desktop rather than centralized and connected to the bigger data ecosystem within a company by leveraging modern server or cloud systems.”
Improving Efficiency in Routine PFAS Analysis Using EPA Methods 533 and 1633A
In this webinar, explore a practical approach to PFAS analysis using EPA Methods 533 and 1633A, with application data illustrating low-level quantitation, accuracy and precision in quality control samples, chromatographic separation of challenging inView Webinar
This lack of flexibility makes it difficult to align outputs with downstream systems such as laboratory information management systems, ELNs, or data warehouses. As a result, organizations often rely on internal IT teams to reformat and manually transfer data—introducing inefficiencies and potential errors.
Expressionist addresses this through an open architecture designed for integration. Key capabilities include:
- Enterprise system embedded within the corporate IT landscape, not isolated on a desktop
- Application programming interfaces (APIs) for system connectivity
- Plugin-based customization for tailored outputs
- Automated reporting formats compatible with downstream systems
“Expressionist was always built as an open Enterprise system, so with that, it's always been easier to integrate with other systems.” — Aude Tartière.
“You can integrate two products more easily using the API, but you can also use what we call plugins, which are essentially customized activities where you can create a customized report in the format that will be automatically ingested by the system,” she added. “It makes it very easy for IT teams to connect products.”
This approach reduces reliance on manual data transformation and simplifies integration across complex digital infrastructures.
Data integration challenges and benefits:
- Rigid software architectures limit interoperability across systems
- Data formatting inconsistencies create integration bottlenecks
- Open systems with APIs and plugins enable seamless connectivity
- Reducing IT dependency accelerates workflow efficiency
The impact of poor data integration on scientific decision-making
How does poor data integration slow down or limit scientific programs?
Tartière highlighted that the issue was not simply about data quantity but rather data usability.
“It's about the quality of the data that you want to transfer; if it’s not well organized, or well annotated, then you don't know what to do with it,” she said. “You might be able to transfer it to your system, but you can't really make any decision with it.”
Three core data quality principles emerged as essential for effective decision-making:
- Well-organized datasets
- Accurate annotation
- Clear context for interpretation
Accelerate your PROTACs drug discovery and development
This eBook shows how an integrated, end-to-end analytical approach helps labs generate reliable, regulatory-ready PROTAC data at every stage of development.View eBook
Without this, even successfully integrated data could fail to deliver value.
“We need high-quality, well-organized, and well-annotated data; otherwise, it’s not going to work. In the context of biotherapeutics, connecting to a molecule-centric workflow system that registers a molecule and tracks it through the R&D phases makes all the difference, and this is where our other products such as Genedata Biologics become highly relevant.”
This insight reinforced a broader industry trend: the shift from data generation to data refinement as the key driver of productivity in drug discovery.
Data quality and decision-making challenges:
- Data volume alone does not drive value
- Poorly organized data hinders decision-making
- Annotation and context are critical for usability
- Data quality directly impacts scientific outcomes
From data generation to data interpretation: The real competitive advantage
Do you think the competitive advantage will come from generating more data or interpreting it better?
Tartière made it clear that the industry had already crossed a threshold at which data generation was no longer the primary challenge.
“MS is a great technology for accumulating data. It's getting high throughput at high resolution, and that's great,” she said.
“That's why people need solutions like Genedata Expressionist, because they need to do something with the data, and they need to be able to make decisions out of it.” — Aude Tartière.
Advancing Host Cell Protein Characterization With Proteomics
Drawing on real-world case studies from industry, attendees will learn practical approaches for achieving deeper proteome coverage, improved quantitative performance, and more robust HCP profiling across complex biopharmaceutical samples.View Webinar
The focus has shifted toward deriving meaningful insights quickly and at scale. Expressionist supported this transition through:
- High-performance, enterprise-grade architecture
- Scalable data processing pipelines
- Integration with R&D data backbones such as Genedata Biologics
- AI-driven solutions such as Genedata’s Vico
Enterprise architecture
A system design approach that ensures software can handle large-scale data processing and integration across multiple organizational systems.
“We [Genedata] can handle the data, clean it properly, process it properly, so at the end you have high-quality results,” she explained.
This pipeline, from raw data to actionable insight, underscores the growing role of AI and automation in augmenting human decision-making.
Data quality and decision-making:
- Data generation capacity has outpaced interpretation capabilities
- Competitive advantage shifts to insight generation
- Scalable infrastructure enables high-volume data processing
- AI-augmented decision-making workflows
As MS technologies continue to evolve, the industry’s focus increasingly turns toward optimizing workflows, ensuring data quality, and enabling seamless integration. Tartière’s insights highlighted a fundamental transformation: success is no longer determined by how much data can be generated, but by how effectively it can be interpreted and applied to make critical and good decisions.
Key takeaways:
- Automation in peptide mapping reduces manual validation burden while preserving scientific oversight
- Open, flexible systems are critical for overcoming data integration challenges
- High-quality, well-annotated data forms the foundation of effective decision-making across biopharma workflows
Add Technology Networks as a preferred Google source to see more of our trusted coverage.
