Tumor biology is spatial.
Spatial metabolomics helps reveal where metabolic changes occur within tissue sections, supporting studies of tumor microenvironment, drug response, and spatial biomarker discovery.
Meet MetwareBio at EACR 2026 Booth #106.
#EACR2026#SpatialMetabolomics
MetwareBio will be at EACR 2026 in Budapest, June 8–11.
Visit us at Booth #106 to discuss spatial metabolomics, spatial lipidomics, spatial proteomics, DIA proteomics, metabolomics, and multi-omics for cancer research.
https://t.co/8fw2e5Npvm
#EACR2026#SpatialOmics
San Diego, see you soon.
A MetwareBio technical specialist will be attending ASMS 2026 to follow the latest discussions in MSI, spatial metabolomics, lipid imaging, DIA proteomics, and omics data interpretation.
#ASMS2026#SpatialOmics
ASMS 2026 is coming up.
MetwareBio's technical specialist will be attending in San Diego to follow advances in MS imaging, spatial metabolomics, spatial lipidomics, DIA proteomics, metabolite annotation, and multi-omics interpretation.
#ASMS2026#MassSpectrometry
Long-chain fatty acids are more than energy substrates—they shape membrane structure, lipid signaling, inflammation, and disease metabolism.
Our latest blog explains LCFA classification, metabolism, disease relevance, and lipidomics analysis strategies.
https://t.co/0f17B6zrMO
Choosing the right extraction method depends on your sample type, target metabolites, and MS workflow. Learn how to select, optimize, and validate metabolite extraction strategies for LC-MS and GC-MS metabolomics.
Read the guide: https://t.co/1oeWexPqY0
Proteomics data is more useful when you know where proteins function. This guide explains how subcellular localization analysis maps DEPs to compartments like the nucleus, mitochondria, ER, and membrane to support biological interpretation.
Read more: https://t.co/h9AU9Ewlrk
Our colleague Chang Deng is visiting Singapore this week, meeting with researchers and partners across the life science community.
Planning an omics project in Singapore or APAC? Let’s talk spatial metabolomics, proteomics, metabolomics, and multi-omics integration.
ORA or GSEA—which pathway enrichment method should you use for omics data?
This guide compares inputs, assumptions, strengths, and limitations to help researchers choose the right approach for proteomics interpretation.
Read the blog: https://t.co/6zzGAI3srH
Don’t miss the chance to connect with us at AACR 2026, Booth 3552!
If you’re working in cancer research and looking for the right partner in proteomics, metabolomics, lipidomics, or multi-omics, come by and chat with the MetwareBio team.
#AACR2026#CancerResearch#Proteomics
How does widely-targeted metabolomics work?
This blog breaks down the workflow, why DDA + MRM matters, and where it is used in biomarker discovery, disease research, and crop science.
Read more:https://t.co/1Ef61hluhG
Not every “top hit” in omics is a strong biomarker candidate.
This blog explains how fold change, p-value, FDR, and VIP answer different questions—and why the smallest p-value alone can mislead feature selection.
Read more: https://t.co/s308FgDO0G
Spatial metabolomics can show where metabolites are in tissue, but identifying what they are is much harder than in LC-MS.
This blog explains a practical MSI identification workflow using accurate mass, MS/MS, spatial patterns, and biological evidence.
https://t.co/krrWf20BFW
New blog: How do metabolites and plant hormones work together to shape plant growth? This article explains how metabolomics helps decode germination, branching, flowering, and senescence. A useful read for plant researchers. Read more: https://t.co/If5p0doxv1
Bad cryosectioning can ruin spatial metabolomics data before imaging even begins.
This blog explains how to improve tissue section quality, choose the right thickness and temperature, and troubleshoot common issues like tearing, curling, and tissue loss.
https://t.co/6p60XqMPc2
New blog is live: Endogenous vs Exogenous Metabolites in metabolomics—plus the messy boundary cases (diet, drugs, microbiome co-metabolism) and a practical framework for source attribution. Read here 👇 https://t.co/OKRRFUuML5
New blog 🚀 Spatial metabolomics data quality starts before the run. We break down cryosectioning vs tissue pressing vs imprinting—pros/cons, best-fit samples, and how to choose for MALDI-MSI or DESI-MSI. Read here: https://t.co/6sc241y7DJ
New blog drop: TiO2 vs IMAC vs antibody-based phosphopeptide enrichment—what each method captures, where bias shows up, and how to choose for your sample + question. If phosphosite depth matters, this is your guide. Read here 👇 https://t.co/cXeIod5mj5
Functional proteomics isn’t “more proteomics”—it’s how you find what proteins are actually doing.
This blog breaks down the 4 pillars (PTMs, interactomics, activity/ABPP, spatial) + when to use each, with a practical workflow you can copy.
Read: https://t.co/aIqLhm4ksY