Your science has never really stopped at proteins. The signal you're chasing also lives in gene expression, in post-translational modifications, in metabolites - but those layers have usually lived in different tools, with different formats, and no easy way to hold them in one place.

Every extra tool is another join to maintain, another export to reconcile, another break in the chain of reasoning behind your results.

This month is about widening what you can bring onto the same connected system.

The goal: more of your omics, analyzed the same way, connected to each other, so your discoveries keep compounding instead of scattering across tools.

Behind every update below is a team that's been heads-down building these agent-ready omics capabilities, and a handful of us who carried them to San Diego to put them in front of the #teammassspec community in person. Hearing how it landed with the community told us we got it closer to right than we'd dared hope - so to everyone who built it, at home and on the road: thank you.

Everything here builds on the personalized support you already get in your Member Success sessions. 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. Early access: drive Mass Dynamics from your own agentic tools

Mass Dynamics is now agent-ready. Our API and MCP (beta) let you run analyses programmatically - from your own pipelines, notebooks, or an AI agent - with the same reproducible records you get in the system's graphical interface.

• API access. Script uploads, analyses, and downloads into your existing workflow.

• MCP + Skills (beta). Point an agent at Mass Dynamics and let it do the heavy lifting, with a set of science-centred skills to get you started.

👉 Reply "Agent Pack, please" for early access to our Agent Pack

2. Bring your gene expression data into the same workspace

Until now, working across proteins and genes meant working across tools. Transcriptomics (gene expression analysis) is now live in Mass Dynamics, so your RNA data can be analysed the same way - and live in the same place - as everything else.

• Upload via MD Format. Bring gene-level data in through the same format and flow you already know.

• A gene-specific QC view. A new module for library size distributions, built for the way gene expression data behaves.

• Your analyses, now at the gene level. Every analysis (apart from dose-response) is available for genes, so the workflow you trust for proteins carries straight across.

• New in the knowledge base. Fresh guides on MD Format for genes, handling zeroes across omics, and gene expression workflows in MD.

Proteins and genes now share one workspace, linked at the entity level - a gene and the protein it codes for sit together, so the connections you'd otherwise rebuild by hand are already there for your discoveries to compound on.

3. See how your modules connect to traits

When you're looking for the biology behind a result, individual entities only get you so far - the story is often in how groups of them move together. Weighted Gene Co-Expression Network Analysis (WGCNA) is now fully available to surface those co-expression modules and tie them back to the traits you care about.

• WGCNA across all environments. The WGCNA dataset is now live everywhere, ready to run.

• Module - Trait Correlation Heatmap: A new plot that shows which groups (or modules) of genes with similar expression patterns are most strongly associated with your experimental traits.

• Turn modules into lists, automatically. Modules become entity lists without manual copying - so a co-expression cluster flows straight into your next analysis.

A faster path from “these things move together” to “here's what that means.”

4. Move through your enrichment results faster

You told us the GSEA dot plot we shipped last month was useful - so we made it work harder. Exploring enriched pathways is now more interactive.

• Click a dot, select the biology behind it. Click any single dot to highlight it and select every associated entity in one move - the same behaviour you already use on the pairwise volcano plot.

• Consistent across your views. One interaction pattern, whether you're in enrichment results or differential expression.

• Separate Gene Ontology views for GSEA: When creating a GSEA dataset, you can now split Gene Ontology results into Biological Process, Cellular Component, and Molecular Function categories, so you can focus on the type of biology behind each signal.

Less clicking around, more following the thread of a result.

5. Early access: more of your omics

This is where the omics expansion is heading - and a lot of it is already in your hands. PTM and MOFA are active now; metabolomics is in beta, so register and we'll get you access.

• Metabolomics (beta). Bring metabolite data onto the platform and analyse it alongside the rest of your omics. Register to get access.

• PTM analysis (now live). Post-translational modification workflows, including new PTM bar-plot views - the same work behind our ASMS poster.

• Factor analysis across your omics layers (MOFA, now live). Find the shared structure running through different omics layers of the same study, so the connections between them become visible rather than inferred.

The direction is simple: every layer of your science, connected in one place - the software your discoveries compound on.

👉 Reply "Metabolomics, please" for early access to our Metabolomics capabilities