Proteomics teams are moving faster than ever, but speed without clarity doesn’t scale.

Over the past month, we’ve focused on one thing: helping teams move quickly without rework, guesswork, or fragmentation as studies grow in size and complexity.

Since our November PULSE, we’ve been working on developments across four areas that define Mass Dynamics 3.0:

• High-confidence productivity - fewer re-dos, more trust in outputs

• Effortless scale - large studies without fragile workflows

• Unified team adoption - shared context across projects and collaborators

• Foundations for what’s next - connecting today’s work to what’s coming in 2026

Here’s what’s new.

1. Smarter reporting without rework

If you’re running multi-dataset studies (e.g. dose response or large comparisons), reporting speed and consistency are critical. We’ve focused on removing the friction that slows teams down and introduces variability.

• Smarter summarized dataset tables. Shape, align, and export complete tables directly - no more copy-paste gymnastics or manual cleanup in Excel.

• Fixed-width tabs for consistent outputs. Lock layouts so figures and tables look the same across screens, collaborators, and exports.

• Clone tabs to move faster. Duplicate a trusted layout to create variants, compare conditions, or build reports without rebuilding views.

• One-click export of all visuals on a tab. Download every rendered plot in a single zipped bundle - ready for decks, reports, or offline review.

• Lock canonical tabs. Protect validated views in shared workspaces so teams can explore confidently without risking the source of truth.

👉 Start exporting from tabs and locking layouts now

2. Effortless scale for large and complex studies

As studies grow, scale breaks down in predictable ways: routine actions take longer, visibility decreases, and small delays compound. We’ve focused on removing those friction points so large studies remain manageable.

• Predictable access to large results.  For large datasets, summary tables now download as pre-generated CSVs, which avoids long, variable wait times as dataset size increases.

• Clearer control over long-running processing. If a dataset processing run stalls or doesn’t behave as expected, you can cancel and restart with adjusted settings instead of waiting indefinitely without visibility.

👉 Work with large datasets more smoothly

3. Clearer QC with broader analytical flexibility

As studies become more complex, confidence in QC and peptide-level decisions matters more than ever. We’ve focused on making QC more transparent, repeatable, and adaptable - without hiding assumptions.

Confidence & transparency in QC

• QC that reflects real peptide-level inputs. QC modules now better support peptide-level datasets generated from tools like Spectronaut and DIA-NN, as well as Mass Dynamics–native formats, reflecting the structure and assumptions of the data you upload.

• More flexible, modular QC views. Expanded QC modules (e.g. distributions, completeness, and more) make it easier to tailor QC to your study design and assess quality at the right level.

• Clearer upload feedback. Right after upload, the platform now tells you which types of analyses your dataset supports (proteins, peptides), so limitations are visible upfront, and not discovered late in analysis.

• Analytical flexibility

  • Percentage guides on violin plots. New horizontal percentage lines help you quickly see where values cluster across conditions.

  • Iterate on preprocessing with confidence. You now have more control over normalization and imputation after upload, including a “None” option. This allows you to regenerate intensity datasets with different preprocessing choices and immediately assess their impact through QC, instead of treating preprocessing as a one-time decision.

👉 Explore the new QC and Peptide options in your workspace

4. Shared context across teams, not just projects

As organizations grow, knowledge often fragments across datasets, projects, and workspaces. We’ve deployed foundational capabilities that help teams stay aligned without relying on tribal knowledge.

• Entity mapping service. A shared layer connecting genes, proteins, and accessions across datasets and projects - forming the foundation for organization-wide scientific context.

• Improved sharing with groups and sub-teams. Create groups to control how datasets and workspaces are shared, so the right people have access without oversharing.

Built on this foundation, Global Entity Text Search (coming soon) will let you search once and discover where an entity appears across datasets and workspaces.

👉 Want early access to global entity search, or help setting up team groups? Reply to this email and we’ll be in touch.