High Content Imaging (HCI) in Spheroids
Introduction to High Content Imaging (HCI) in Spheroids
High-content imaging (HCI) in spheroids is a powerful, histology-like approach that enables detailed analysis of proteins, cells, and tissue architecture within physiologically relevant 3D microtissues. When applied to InSphero’s 3D InSight™ Microtissue Models, HCI captures complex biological responses that closely mirror the in vivo tissue environment.
As a core analytical method across InSphero’s platforms, HCI delivers high-resolution, multiparametric data that support translational, data-driven decision-making from early discovery through drug development.
Our approach: where human-relevance, imaging, and scalability meet
Translational relevance
Single-cell, high-resolution analysis
Comprehensive phenotypic profiling
Scalable and automated
Similar to clinical histopathology, HCI captures changes in cellular composition, morphology, and tissue organization, enabling meaningful connections between in vitro findings and in vivo outcomes.
Confocal HCI provides single-cell resolution within intact 3D spheroids, allowing quantification of cell population–specific responses. Combined spatial and temporal information enables assessment of drug biodistribution, penetration depth, kinetics, and phenotypic differences across tissue compartments.
High-content imaging complements biochemical assays and secreted biomarker readouts, delivering a holistic view of compound activity that integrates structural, functional, and molecular endpoints.
Fully automated HCI workflows scale seamlessly from deep characterization of individual clinical candidates to mid-throughput screening in drug discovery programs.
Why High Content Imaging with InSphero?
Imaging-compatible 3D in vitro cell models
Quantify specific cell populations, spatially-defined events and morphological changes with confidence in InSphero’s uniform and reproducible spheroid models
High-resolution imaging in 3D and 4D
Exploit the full extent of 3D biology with volumetric and time-lapse analysis
End-to-end 3D workflows
Experience seamless workflows from 3D model generation to data analysis with InSphero’s 3D-optimized pipelines (platform)
Tailor-made and intuitive data reporting
Gain biological insights from single endpoints or from comprehensive phenotypic profiles.
Our Automated 3D Imaging Workflow
InSphero offers end-to-end high-content imaging workflows that bring together all critical components required for successful 3D imaging and analysis. From standardised scaffold-free 3D microtissues production to optimized imaging and data analysis, our workflows are designed to deliver robust, reproducible results.
Established 3D/4D Imaging Pipelines
InSphero has established advanced 3D and 4D high-content imaging pipelines that enable detailed characterization of complex biological processes within physiologically relevant microtissues.
By combining multiplexed staining, high-content imaging, and quantitative analysis, our imaging pipelines capture cellular composition, functional events, spatial organization, and disease-related phenotypes within intact 3D in vitro models.
Multiplexed Cell Population Analysis
Colocalization Analysis
Cell population–specific markers enable multiplexed analysis of heterotypic 3D models by labelling individual cell types within the same sample. Combined with robust tissue clearing, high-resolution 3D imaging, and volumetric image analysis, up to four cell populations can be imaged and characterized simultaneously to assess cell population distributions, cellular reprogramming, transduction efficiency, and immune cell infiltration.
Combining cell population markers with functional endpoints enables quantification of physiologically relevant events within specific cell populations. Using high-resolution whole-spheroid imaging, tissue clearing, and 3D volumetric analysis, the pipeline quantifies sub-populations with colocalized biomarker signals, including events such as proliferation, apoptosis, mitochondrial and oxidative stress.
Spatial and Temporal Characterization
Disease Phenotype Characterization
Labelled cell populations, as well as drug candidates, can be analyzed by their spatial localization within the 3D microtissue. When combined with live imaging, time-dependent changes can also be quantified, such as the depth and speed of immune cell migration or therapeutic antibody penetration.
Classical histological markers and stains enable the characterization of tissue and cellular structures associated with healthy or diseased states, such as extracellular matrix components or lipid droplets. Combined with automated imaging, AI-assisted segmentation, multiparametric image analysis, and phenotypic clustering, these translational assays help bridge in vitro and in vivo data and provide insights into mechanism of action.
High-Content Imaging Across Diverse Research Applications
High-content imaging with InSphero’s 3D microtissue models supports a wide range of research applications, from safety assessment to disease modelling and therapeutic discovery.
Explore selected case studies demonstrating how our HCI workflows have been applied in areas such as liver safety, liver disease, immuno-oncology, and islet biology to generate deeper insights into complex tissue responses.
Multiplexed Cell Population Analysis
a, b, and d cell distribution in pancreatic islet microtissues
- Model: 3D Insight™ Human Islet Microtissues
- Labels: DAPI (nuclei), Anti-insulin b-cells), anti-glucagon (a-cells), anti-Somatostatin (d-cells)
- Imaging method: Z-stack acquisition, 3D volumetric analysis
- Microscope: Yokogawa CQ1
- Plate format: Akura™ 384 ImagePro
Quantification of α-, β-, and δ-cell populations in pancreatic islet microtissues. Islet microtissues were immunolabeled with DAPI to identify all nuclei, anti-insulin to identify β-cells, anti-glucagon to identify α-cells, and anti-somatostatin to identify δ-cells. Positively labeled cells in each channel, shown as individual dots, were counted and expressed as a percentage of the total cell number based on the DAPI channel.
Multiplexed Cell Population Analysis
Viral transduction efficiency and tumor cell reprogramming
- Model: 3D Insight™ Human T98G- PIB Tumor Microtissues
- Assay Processing: fix, label, and clear
- Imaging method: Z stack acquisition, aggregated Z section analysis
- Microscope: Yokogawa CQ1
- Plate format: Akura™ 384 ImagePro
- Reference : Ascic et al Science 2024
High-content 3D imaging-based quantification of viral transduction and reprogramming efficiency in T98G spheroids. A tumor spheroid model was generated from T98G cells transduced with a viral vector expressing PIB transcription factors and mCherry at increasing multiplicities of infection (MOI). Reprogramming efficiency was quantified as the percentage of CD45⁺/mCherry⁺ cells
Colocalization Analysis
Proliferation events in selected cell populations in islet microtissues
- Model: 3D Insightä Human islet microtissues
- Labels: DAPI (all cells), anti-NKX6.1 (b-cells), EdU (proliferating cells)
- Imaging method: Z stack acquisition, 3D volumetric analysis
- Microscope: Yokogawa CQ1
- Plate format: Akura™ 384 ImagePro
- Reference: Title et al. Frontiers in Endocrinology 2022
Quantification of β-cell-specific proliferation in heterotypic pancreatic islet microtissues. Microtissues were labeled with anti-NKX6.1 to identify β-cells and EdU to detect proliferating cells. Rare NKX6.1⁺/EdU⁺ cells were identified by signal co-localization, enabling comparison of proliferating β-cells with proliferating non-β-cells to determine whether the response is cell-type specific or generalized.
Disease Phenotype Characterization
Lipid droplet quantification and classification of steatosis phenotypes in Liver Disease
- Model: 3D Insightä Human Liver MASH
- Labels: DAPI (nuclei) and BODIPY (lipid droplets)
- Imaging method: Z-stacks, 2D MIP Analysis
- Microscope: Yokogawa CQ1
- Plate format: Akura™ 384 ImagePro
AI-assisted lipid droplet analysis and phenotypic clustering in liver microtissues. Liver microtissues were stained with DAPI to visualize nuclei and BODIPY to detect lipid droplets. AI-assisted segmentation using the IKOSA AI platform classified lipid droplets as microvesicular or macrovesicular. A 3D UMAP projection based on 184 imaging features identified four reproducible phenotypes across independent experiments, supporting comparison of compounds with similar or distinct mechanisms of action. Data points colored by compound treatments (compound data not yet published).
Disease Phenotype Characterization
Tissue characterization of the MASH phenotype
- Model: 3D Insightä Liver MASH
- Labels: DAPI (nuclei), anti-Col-I (ECM), anti-fibronectin (ECM), BODIPY (lipid droplets)
- Imaging method: Confocal z stack
- Microscope: Yokogawa CQ1
- Plate format: Akura™ 96 Spheroid Microplate
HCI-based fibrosis analysis in cleared 3D MASH liver microtissues. Liver microtissues were stained for collagen I and multiplexed with fibronectin to assess fibrosis-associated extracellular matrix deposition. 3D image segmentation and analysis were performed using the ZEISS arivis Pro platform.
Spatial and Temporal Characterization
Quantification of antibody penetration over time
- Model: A549 tumor
- Labels: DAPI (nuclei), anti- b-integrin
- Imaging method: Z stack acquisition, analysis of the equatorial z section
- Microscope: Yokogawa CQ1
- Plate format: Akura™ 384 ImagePro
Reference: 3D InSight™ Tumor Penetration Screening | by InSphero
Quantification of antibody penetration in live 3D InSight™ Tumor Microtissues. Live tumor microtissues were exposed to an anti-β-integrin antibody for up to 24 hours. Samples were harvested at selected timepoints, and antibody penetration depth was quantified using a fluorescently tagged secondary antibody. Equatorial z-sections were used to assess antibody penetration at each timepoint.
Related Services
Technologies for RNA Sequencing in Spheroids
Low-input RNA-Seq
Low-input RNA-Seq is designed for situations where the RNA quantity is limited, making it ideal for small 3D spheroid models. It focuses on sequencing mRNAs, capturing a broad range of poly-A transcripts with high sensitivity for detecting differential gene expression. This method is species-agnostic making it suitable for various research models. It is a versatile option for small to mid-size projects requiring unrestricted and in-depth gene expression analysis from minimal input material.
Whole Transcriptome TempO-Seq
Whole Transcriptome TempO-Seq assay is a sequencing method that targets a pre-selected set of ~19-22K mRNAs. However, it is limited to human, rat, and mouse species. Its cost-efficiency and capability for high-throughput make it a practical choice for mid- to large-size projects with limited budgets aiming at robust and sensitive transcriptome-wide gene expression profiling.
Organoid DRUG-Seq
Organoid DRUG-Seq is optimized for high-throughput screening of drug responses in 3D spheroids, allowing researchers to efficiently profile gene expression changes in response to different treatments. It is also species-agnostic, making it versatile for various drug testing scenarios. Its scalability and affordability make it an excellent choice for large-scale projects aiming at screening the most prominent gene expression changes in a cost-effective fashion.