agent.bio

A public, interactive agent sandbox for five major spatial kit/machine types: Takara Seeker, Vizgen MERFISH, AtlasXOmics DBiT-seq, 10X Xenium + 10X Visium

We’re releasing agent.bio: an interactive sandbox with access to five demo flows, each tailored to specific spatial biology technology: Takara Seeker, Vizgen MERFISH, AtlasXOmics DBiT-seq, 10X Xenium and 10X Visium.

Try it here: agent.bio

We’ll go over:

• Our roadmap for reliable and widely deployed spatial agents

• Important considerations while playing with these sandboxes

• Video demos of each agent

Our roadmap for reliable and widely deployed spatial agents

LatchBio develops agents for spatial biology data that allow scientists to direct their own analysis with natural language. By tailoring each agent to specific spatial kits and machines, and iterating relentlessly on this reduced scope, we believe it’s possible to build accurate systems that can be used to make expensive scientific decisions.

This release represents a shippable intermediary towards this goal, both to show technical progress and help others see why we’re so excited about this direction. The path ahead is a long tail of soluble engineering work: continued progress on benchmarks, purpose built infrastructure and technology specific heuristics encoded into prompts/evals.

Important considerations while playing with these sandboxes

We’re developing two agentic modes, “step-by-step” and “proactive”, that provide control over how often the agent will check-in, ask questions and provide figures or statistics for scientists drivers to sanity check intermediate steps. The sandboxes default to “proactive” to provide a nice first impression of end-to-end functionality, but we encourage biologists to start with “step-by-step” for any work of value. More on this soon.

Furthermore, the sandboxes were built for specific datasets and tasks. You can try to use your own data, and the system will likely generalize accurately on some tasks, but we provide no guarantees on performance quite yet.

Video Demos

The following videos and descriptions motivate some of the practical end-to-end workflows possible with the system. We highly encourage the reader to play with each themselves.

Takara Seeker

Ovulation failure affects over 10% of infertility cases in women of reproductive age in the United States, yet it is exceptionally difficult to study because ovulation unfolds within tiny, spatially restricted niches and evolves on the scale of minutes.

Using a Takara Seeker 3x3 kit, Mantri et. al built a spatiotemporal molecular atlas of the ovulating mouse ovary across 8 different time points.

To replicate the analysis, the Takara agent ingested raw H5AD files, removed off-tissue beads, computed QC metrics and filtering, performed clustering, and ran differential gene expression. The agent then manually reasoned through top marker genes for each cluster and identified cell types key to the ovulation process, such as mural and cumulus granulosa cells, mesenchymal subtypes, oocytes, and endothelial and immune populations. A scientists can then ask the agent to count the atretic or antral follicles.

Vizgen MERFISH

The mouse brain contains highly organized cellular circuits, yet these structures are difficult to resolve without single-molecule spatial data. MERFISH overcomes this barrier by capturing the precise locations of individual transcripts across intact tissue.

Using a MERFISH mouse brain dataset, the agent built a spatial neighbors graph, computed Moran’s I, and performed neighborhood enrichment and co-occurrence analyses. It identified spatially variable genes such as MYH11, SLC17A7, and DAAM2, each marking distinct anatomical regions; MYH11 was correctly enriched in blood vessel-associated cells, consistent with its vascular smooth muscle role. Neighborhood enrichment further recovered expected spatial organization, including co-localization of excitatory and GABAergic neurons and strong oligodendrocyte self-enrichment, matching known cortical and subcortical structure.

AtlasXomics DBiT-seq

Tumor heterogeneity is a major challenge in cancer, arising both between patients and within a single tumor. Changes in chromatin accessibility are a key driver of this variability because they alter gene expression, cell identity, and interactions in the tumor microenvironment.

Using AtlasXomics spatial ATAC-seq (DBiT-seq) from a stomach cancer sample and a matched control, the agent ran an ArchR-based workflow to identify differentially accessible chromatin regions, peaks, and motifs.

In tumor tissue, the agent detected strong enrichment of CTCF/CTCFL motifs, pointing to disrupted 3D chromatin organization, a hallmark of many cancers. It also found IRF1 enrichment, consistent with immune infiltration or localized inflammatory signaling. By contrast, the control tissue was enriched for FOXA1 and TEAD motifs, which support normal epithelial identity and Hippo pathway-mediated tissue homeostasis.

10X Xenium

Alzheimer’s disease reshapes the brain through localized inflammatory niches and region-specific neuronal vulnerability. These patterns only become visible only with high-resolution spatial transcriptomics.

Using a Xenium dataset from a TgCRND8 Alzheimer’s mouse brain section, the agent loaded the raw outputs, built an AnnData object, performed clustering and differential expression, and annotated cell types using a controlled mouse brain vocabulary.

The analysis recovered five major neural populations: astrocytes, excitatory neurons, inhibitory neurons, oligodendrocytes, and microglia. It also detected Alzheimer’s-relevant signals such as strong Gfap spatial enrichment marking reactive astrocytes and Hexb expression marking activated microglia. Finally, the workflow identified 21 spatial domains that correspond to known anatomical regions and disease-affected microenvironments, including glial clusters surrounding neurons, a characteristic pattern in Alzheimer’s models.

10X Visium

Colorectal cancer is shaped by tightly packed tumor, stromal, and immune niches that often sit only a few microns apart, making high-resolution spatial profiling essential for understanding disease architecture.

Using the Visium HD colorectal cancer dataset with more than 500,000 spots, the agent converted Space Ranger outputs into an H5AD and launched a GPU-accelerated RAPIDS workflow to handle the dataset at scale. The full pipeline from QC to clustering to differential expression completed in under a minute.

The agent then inspected top marker genes for each cluster and assigned epithelial, stromal, and immune cell types, reconstructing the organization of the colorectal tumor microenvironment.

Path Ahead

We’ve learned a lot about biology specific agent engineering in the past month building and saturating our benchmarks (eg. what is the analogue of a “code diff” for spatial analysis). Expect more educational material on these topics in the coming weeks.