Resources: Posters

Integrating longitudinal imaging and transcriptomics in single cells using CellCage™ Technology reveals cellular heterogeneity beyond gene expression

February 24, 2026

AGBT General Meeting

Pier Federico Gherardini, Ph.D., Head of Computational Biology

Keywords: Multi-modal analysis, Single-cell transcriptomics, Developmental trajectories, Non-destructive analysis

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Understanding cellular heterogeneity requires correlating a cell's behavioral history with its genetic identity. This study presents a multimodal workflow that integrates continuous live-cell imaging with downstream transcriptomics to capture functional states and behavioral context without dissociation.

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Case Study: Functional Profiling of Microglia in Neuroinflammation

Link microglial behavior to gene expression at single-cell resolution, for insight into neuroinflammation, drug response, and immune dysfunction in CNS disease.

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Phagocytosis assay in CellCage enclosures - (click to expand image).

Researchers used the Cellanome R3200 to enclose individual microglia with fluorescent particles and track phagocytosis over 12 hours via fluorescent imaging. Each cell’s transcriptome was then sequenced, linking activity levels to gene expression.

What they found:

High-activity microglia upregulated genes in complement signaling, lipid metabolism, and lysosomal function–key pathways in neuroinflammation and repair.

Why it matters:

This approach overcomes key limitations in standard assays by capturing phagocytic function and gene expression in the same individual cells without dissociation, pooling, or inference. It enables a direct, scalable readout of immune heterogeneity, and reveals the transcriptional programs driving effective or impaired microglial responses.

‍What’s next:

Extend to co-cultures by layering enclosed microglia over intact neuronal networks. Study how cell-cell interactions shape phagocytic behavior and fate. Combine with cytokines, CRISPR libraries, or immunotherapies to generate time-resolved, multi-modal datasets that can be used for MoA analysis, early biomarker discovery, and AI-guided modeling in CNS disease.

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Case Study: Modeling Synapse Formation and Developmental Trajectories in 3D

Track development, function, and gene expression in intact neurospheres, a human-relevant 3D model increasingly vital as regulators move away from animal studies.

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Neurospheres in windowed cages that allow them to reach out to form a synapse - (click to expand image).

Stem-cell-derived neurospheres offer a robust 3D model of early brain development, but standard assays disrupt their structure and miss critical dynamics.

Approach:

Using the Cellanome R3200, the research team explored,

Hundreds of intact neurospheres (100–200 cells each) were cultured inside individual CellCage™ enclosures.
Axon extension, synapse formation and calcium activity were tracked over multiple days.
End-point RNA-Seq was linked back to each neurosphere’s functional behavior.
UMAP clustering revealed lineage-specific gene programs, validated by fluorescent markers.

‍What's next:

This lays the groundwork for CRISPR-based multimodal screens to probe mechanisms of development, degeneration, and repair within preserved 3D architecture.

‍Why it matters:

As the FDA and others move to reduce reliance on animal models, human-relevant in vitro systems like this are increasingly essential.

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FAQ's

What are the advantages of integrating longitudinal imaging with transcriptomics?

Integrating these modalities allows for the correlation of a cell’s behavioral history—including movement and morphology—with its final transcriptomic state. This reveals how developmental trajectories and functional changes unfold over time at single-cell resolution without the confounding effects of cell dissociation.

Does the technology identify cellular states beyond standard gene expression?

Yes. By capturing the temporal and spatial context of living cells, the technology identifies functional states and behavioral patterns that are not visible through transcriptomic data alone, offering a more comprehensive understanding of cellular heterogeneity.

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