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Explore peer-reviewed studies, technical notes, and posters that show how spatial and multimodal data can reveal mechanisms, guide decisions, and accelerate discovery.

Publications for advancing spatial insight

Publications

See how research teams use Weave to align modalities, analyze multicellular environments, validate mechanisms, and compare cohorts with reproducible workflows.

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Research Paper

Large-scale, spatially resolved panoramic CRISPR screening in native tissue environments using Perturb-DBiT

What if CRISPR screens could reveal not just which genes matter, but exactly where in a tumor they matter — and how they rewire the entire RNA landscape in situ? That is precisely what Perturb-DBiT now makes possible.
Cancer Research
spatial technologies
News
Research Paper

Extraction of the polysorbate 20 and 80 fingerprint via generative modeling

This transforms polysorbate analysis from labor-intensive peak-by-peak workflows into an objective characterization tool suited for quality control, batch selection and degradation monitoring.
Cell segmentation / typing
Exploratory Analyses
Research Paper

White paper - Xenium in situ analysis and reproducibility study of multiple carcinomas via an end-to-end spatial multi-omics platform.

This white paper is an independent study conducted in collaboration with BioChain Institute Inc. examining the reproducibility of the Xenium spatial transcriptomics platform from 10X Genomics.
spatialomics
spatial technologies
Exploratory Analyses
Cancer Research
Spatially-aware Clustering
Research Paper

Toward Omics-Scale Quantitative Mass Spectrometry Imaging of Lipids in Brain Tissue Using a Multiclass Internal Standard Mixture

This paper demonstrates an approach for conducting quantitative mass spectrometry imaging (MSI) of lipids directly from brain tissue sections. This greatly expands the utility of MSI for spatial lipidomics.
lipidomics
mass spectrometry imaging
central nervous system
Exploratory Analyses
Research Paper

Quantitative MALDI Imaging of Aspirin Metabolites in Mouse Models of Triple-Negative Breast Cancer

This study refined quantitative MALDI imaging (QMALDI) to map aspirin metabolites, especially salicylic acid (SA). Precise spatial alignment, integration, and quantification of QMALDI, histology, and immunofluorescence images allowed accurate evaluation of the spatial distribution of SA in tissue regions, and helps with the further development of aspirin as an activatable MRI contrast agent.
Cancer Research
Exploratory Analyses
mass spectrometry imaging
spatial technologies
Research Paper

Integration of Multiple Spatial Omics Modalities Reveals Unique Insights into Molecular Heterogeneity of Prostate Cancer

In this preprint, we integrated spatial transcriptomics, mass spectrometry-based lipidomics, single nucleus RNA-seq and histomorphological information from human prostate cancer patient samples. This provided novel insights into correlating genes and lipids linked to distinct cell populations and histopathological disease states, as well as comparisons across samples in a tissue cohort.
Cancer Research
Exploratory Analyses
lipidomics
mass spectrometry imaging
spatial transcriptomics
Research Paper

Incorporating morphology via deep learning improves classification performance of MALDI imaging for skin lesions

This presentation addressed how combined analysis of mass spectrometry imaging data and digitized H&E histological images produced more accurate classification skin samples versus approaches that used either the histological images or MSI data on their own.
Cell segmentation / typing
Research Paper

Integrating Ambient Ionization Mass Spectrometry Imaging and Spatial Transcriptomics on the Same Cancer Tissues to Identify RNA–Metabolite Correlations

This study presents a novel workflow for mapping the location and abundance of mRNA and metabolites from the same tissue section, allowing identification of thousands of spatially correlating genes and metabolites.
lipidomics
mass spectrometry imaging
Exploratory Analyses
Cancer Research
spatial technologies
Research Paper

Classification of Small Blue Round Cell Tumors by integrating peptide and N-glycan mass spectrometric profiles

How do different machine learning models perform in the classification of tumours when trained on information from peptide or N-glycan profiles? This study investigated what happens when commonly used machine learning models were trained on peptides and histology profiles, N-glycans and histology, and the integration of all three.
Cancer Research
mass spectrometry imaging
spatial technologies
spatialomics
AI/ML

FAQs

Understanding Weave®, spatial biology, and multimodal analysis

Still have questions?

Does Weave support collaboration across teams and projects?

Weave supports multi-user, multi-team projects. With our shareable and compliant workspaces, different team members can collaborate in real-time on the same datasets in one governed workspace. Biologists, pathologists, computational biologists, and data leaders can interactively visualize and explore the same measurements and derived results, for both single and multi omics datasets. True democratization of spatial biology insights.

Can Weave be operationalized at the enterprise level?

Yes. Weave is enterprise-ready – not only do we have ISO 27001 and ISO 27018-certified data security and handling of PII, but also SSO integration with industry-standard federated authentication servers. In addition, we have scalable storage and computation resources with hosted and on-premise deployment options with SDK and CLI interfaces for systems integration. Our users routinely manage up to petabytes of spatial biology and associated data within Weave.

Can I see examples of published work using Weave?

Yes. Browse our publication section above to explore peer-reviewed studies, application notes, posters, and collaborative projects demonstrating how Weave supports spatial and multimodal research across disease areas.

How do I know if Weave is right for my project?

If your research involves tissue architecture, spatial biomarkers, spatial multi-omics integration, PK/PD readouts, or mechanistic interpretation, Weave provides a reproducible and scalable analytical framework. You can start with a single study and expand to multi-cohort programs.

Is it possible to automate recurring analyses?

Yes. Using the Weave SDK, our team can help you integrate your in-house pipelines, build reusable workflows, visualization modules, or reporting templates to reduce manual work and support high-throughput projects.

Can Aspect Analytics help if we don’t have internal computational resources?

Yes. Aspect Analytics offers data analysis services from our expert team who have over a decade of experience in spatial bioinformatics and data analysis. Weave is entirely cloud-based, so you don’t need local processing power or specialized infrastructure to analyze spatial multi omics data. Taken together, our team can remotely support you with study-level data integration, cellular neighborhood analysis, workflow development and more, while your teams access the results and reusable workflows directly in the cloud. This collaboration keeps your projects moving even without in-house computing or engineering support.

How does Weave support translational and drug development teams?

Weave enables spatial PK/PD analysis, MoA exploration, responder vs. non-responder comparisons, and more. The platform produces deeper insights, and makes it easy to interpret and share across preclinical and translational groups. By providing a collaborative and secure environment, Weave turns massive, multi-modal spatial datasets into actionable insights to accelerate your drug discovery and development pipelines.

What makes Weave different from other spatial analysis tools?

Weave provides the computational foundation to unify and analyze data across the multiple layers of spatial biology, histology, and metadata. It supports multimodal co-registration, comparative cohort analysis, model-ready outputs, and custom workflow development, bringing clarity to complex spatial data. Weave is also certified against the ISO 27001 and ISO 27018 standards for information security and handling of Personally Identifiable Information (PII).

What kinds of biological questions can Weave help answer?

Weave is used to explore cell-cell interactions, cellular neighborhoods & spatial niches, ligand–receptor communication, PK/PD and drug distribution, pathway activity, and tissue-level mechanisms that drive disease progression or therapeutic response. Weave enables finding spatial and/or molecular signatures indicative of disease status, treatment response, etc.

How does Weave help with multi-site or multi-cohort studies?

Weave standardizes quality control, spatial alignment and integration, and lineage tracking across sites, batches, and instruments. This creates reproducible, comparable datasets that support biomarker discovery, patient stratification, and translational analysis.

Can I integrate data generated outside of Weave?

Yes. External transcriptomics, proteomics, MSI, or histology annotation files can be ingested and integrated. You can also load data that has been preprocessed using your in-house pipelines, or cell segmentation results from a pipeline you have optimized for your use-case. This lets you build complete, multi-layered datasets even when parts of the workflow happen elsewhere.

Can Weave analyze data from different platforms or vendors?

Yes. Aspect Analytics is vendor-neutral and works with all major spatial biology technologies. Supported platforms include Xenium, Visium, CosMx, COMET, PhenoCycler, IMC, assorted microscopy vendors, MALDI and DESI-MSI, and more. Core capabilities of Weave include analysis of large-scale projects using single or a limited number of spatial assays, or cross-platform harmonization across different modalities for comprehensive multi-omic characterization.

Do I need advanced computational expertise to use Weave?

No. Biologists, pathologists, and computational scientists can all work within Weave. The Weave GUI supports visual analysis without coding, while the Weave SDK allows computational users to extend workflows or build custom analyses.

What kinds of data can I analyze with Weave?

Weave software supports histology, spatial transcriptomics, spatial proteomics, multiplex imaging, mass spectrometry imaging (MSI), and complementary single-cell omics at scale. These layers can be aligned, harmonized, and compared within one governed analytical backbone.