As studies become more complex, small sources of ambiguity can accumulate - uncertain PTM assignments, difficulty tracking trends across time or dose, or missing metadata needed to interpret patterns. These aren't dramatic failures. They're the kind of friction that slows teams down gradually.

This month's updates focus on making those assumptions more explicit and easier to work with.

The goal of these new features:

• clearer peptide-level interpretation

• more flexible ways to explore intensity trends

• better control over sample metadata

• a powerful foundation for programmatic access

Everything here builds on the personalized support you already get in your Member Success sessions with our team.

Feel free to raise any of these updates in your next session if you want help making the most of them.

Here's what's new.

1. More confident modified peptide assignments

When working with modified peptides, confidence in PTM localization matters. Low-probability assignments introduce noise that complicates downstream interpretation.

You can now filter those out directly on the platform.

• Localization-probability filtering for PTMs. Generate a filtered peptide dataset that excludes modifications below a user-defined localization probability threshold. The filtered result becomes a new dataset linked to the original peptide input, so you can compare filtered and unfiltered analyses side by side.

• Supported DIA-NN inputs. When DIA-NN report files include localization probability fields, those values are available for filtering.

• Localization probability distribution plot. A new visualization shows the distribution of localization probabilities alongside intensity values, helping you assess where to set your threshold.

This allows uncertain modification assignments to be removed while preserving the original dataset for reference.

👉 Generate a PTM-filtered peptide dataset in your workspace

2. Visualize abundance trends over multiple experimental conditions

Understanding how intensity changes across conditions often requires looking beyond static comparisons. A new line plot module lets you track protein and peptide intensities across ordered experimental variables.

• Intensity trends over metadata fields. Plot protein or peptide intensities against timepoints, doses, or other experimental variables defined in your sample metadata.

• Integrated protein and peptide tracking. Inspect how individual peptides belonging to the same protein behave across conditions.

• Flexible grouping and colouring. Group and colour plots by metadata fields to compare trends across experimental factors.

• Multi-level grouping support. Combine multiple metadata variables to visualise complex experimental designs in a single view.

This makes longitudinal or dose-response behaviour easier to inspect directly within the platform.

👉 Explore intensity trends using the new line plot module

3. Capture PCA insights without leaving Mass Dynamics

Principal component analysis often reveals structure not captured in the original metadata. Previously, incorporating those insights required editing metadata outside the platform. Now you can update sample metadata directly from PCA selections.

• Select samples directly in PCA plots. Highlight groups of samples and annotate them without leaving the analysis view.

• Create or update metadata columns. Add batch labels, conditions, or other experimental annotations on the fly.

• Save PCA coordinates as metadata. Store principal component coordinates directly in the sample metadata table for downstream use.

• Reuse annotations across analyses. Newly created metadata can immediately be used in grouping variables, visualisation modules, or further analyses.

A faster way to capture insights from exploratory analysis and apply them across the rest of your workflow.

👉 Annotate sample metadata directly from PCA

4. Programmatic access with API 

For teams integrating Mass Dynamics into automated pipelines or custom workflows, an enhanced API is now available. This release simplifies how you interact with the platform programmatically and brings endpoint naming in line with how the platform works today.

• Cleaner endpoint naming. Endpoints have been updated to reflect current platform terminology.

• Updated md-python package. The Python client library has been updated to support the enhanced endpoints, so existing scripts can be migrated with minimal changes.

• Incremental stability improvements. Various fixes and refinements across the API surface to improve reliability for automated workflows.

If you're building on top of Mass Dynamics programmatically, these API enhancements is a cleaner starting point. If you're not, and keen to explore, talk to the Mass Dynamics sales team.

👉  Explore the API documentation: Public GitHub Repository and API Documentation  

5. Get started with differential expression faster

Differential expression remains one of the most widely used workflows on the platform. To help teams move from dataset upload to results more efficiently, we've added a new step-by-step video guide.

• Concise walkthrough from setup to results. The guide covers dataset configuration, comparison setup, and interpretation of outputs.

• Designed for new teams and new users. Reduces setup friction for teams adopting the workflow for the first time.

• Available on demand. Watch it at your own pace and revisit specific steps as needed.

👉  Watch the differential expression video guide