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Biospective has industry-leading expertise in providing microglia histology & advanced image services, including multiplex immunofluorescence (mIF) staining using a variety of antibodies for comprehensive microglial phenotyping. We have developed proprietary image analysis and image visualization capabilities for microglial morphology, cell-cell interactions, and spatial analysis.

Explore our Services

What Contract Research Services does Biospective Offer for Microglia Staining & Analysis?

Multiplex immunofluorescence staining, segmentation, morphological and spatial analysis of microglia in tissue sections from rodent models.

Microglia are central determinants of central nervous system (CNS) homeostasis and disease progression across major indications, including:

  • Alzheimer’s disease (AD)

  • Tauopathy-related dementias (e.g. FTD, CBD, PSP)

  • Parkinson’s disease (PD)

  • Amyotrophic Lateral Sclerosis (ALS)

  • Multiple Sclerosis (MS)

Quantitative analysis of microglial features, including density, morphology-based activation state scoring, and neuron-associated contact topology, is critical for measuring disease progression and therapeutic response. Biospective has developed robust multiplex immunofluorescence (mIF) staining and automated whole-slide quantification workflows optimized for pathology-centric microenvironment profiling and microglia–neuron interaction endpoints in rodent CNS tissues.

Our Microglia Staining & Image Analysis Capabilities

Iba1-based Microgliosis Quantification

  • Whole-slide Iba1 stain density quantification and regional burden within specified regions-of-interest, minimizing sampling bias and supporting cohort-scale comparisons.

Microglia Segmentation and Morphology-based Activation Profiling

  • Whole-slide detection and classification of microglia.

  • High-content morphometrics extracted from Iba1-positive cells and analyzed with trained, validated machine-learning models to identify subtle state shifts.

  • Outputs include soma size and shape remodeling, process length and branching architecture, enabling objective morphology-driven subclassification and continuous state scoring.

Multiplex Phenotyping Aligned to Mechanism-of-Action

  • Microglia state-marker panels combined with Iba1 in mIF, typically up to four targets plus DAPI, optimized for the rodent model, tissue type, and endpoint strategy. Markers can be quantified as stain density in regions-of-interest and within segmented cells to obtain cell-expression profiles. Common markers include:

    • Microglia identity and homeostatic state: TMEM119, CD11b

    • Phagolysosomal burden and phagocytic load: LAMP1, CD68

    • Reactive inflammatory activation and immune context: CD45, TREM2

    • Effector-pathway context: iNOS, complement components (for example C1q)

    • Inflammasome engagement: ASC, cleaved caspase-1

    • Purinergic signaling: P2RY12

Pathology Microenvironment Analysis

  • Pathology and microenvironment markers can be integrated to quantify how microglial state varies with local pathologic burden and proximity to pathology-defined foci, enabling spatially resolved niche phenotyping within inflammatory microenvironments. Channels may include:

    • AD models: Aβ plaque markers (fibrillar amyloid, 6E10/4G8, pFTAA) and tau markers (AT8, PHF1, MC1, p-Tau217, cleaved tau N368/Asp412)

    • ALS models: phospho-TDP-43 (p409/410), human TDP-43, total TDP-43

    • MS models: myelin integrity markers (e.g. MBP)

    • Structural and vascular markers: GFAP, laminin

  • Outputs include profiling of metrics such as stain density and microglial morphology as a function of distance to the pathology, and colocalization of specific markers.

Quantification of Microglia-Neuron Direct Interactions

  • By adding a neuronal marker (e.g. NeuN), direct contacts between microglia and neurons can be characterized.

  • Both the size of the interaction and the location (microglia process to neuronal soma or soma-soma) can be quantified automatically.

What is Biospective’s Workflow for Staining & Quantitative Analysis of Microglia?

Well-established protocols for brain sample preparation, staining, slide scanning, and quantitative image analysis.

Our Process for Microglia Staining & Analysis

At Biospective, we have implemented a standardized, highly reproducible multi-step process for staining and analysis of microglia from formalin-fixed brains:

  1. Sample Preparation

    •  High-precision microtome sectioning or cryosectioning of FFPE or fixed-frozen brains.

    • Custom antigen retrieval protocols optimized for Iba1, NeuN, and microglia activation markers, maximizing signal-to-background to support detection of subtle features required for quantitative image analysis. Retrieval conditions are further customized for any additional antibodies included in the multiplex panel. We routinely perform formic acid retrieval, heat-induced retrieval (HIER), enzymatic retrieval, or a combination of these methods.

    • Stringent quality control (QC) of staining quality and specificity as well as tissue integrity.

  2. Staining (IHC or Multiplex IF)

    • Marker panels are configured based on the desired analysis, often combining:

      • Iba1 (microglia)

      • NeuN (neuronal soma; when cell-to-cell interaction endpoints are required)

      • Disease model-relevant pathology (e.g. Aβ, p-tau, p-Syn129, phospho-TDP-43)

      • Microglia state or pathway markers (e.g. CD68, TREM2, ASC)

      • DAPI (nuclei)

    • Advantages of Multiplexing

      • Multiplexing enables cell-type–specific analysis of the microenvironment on a single slide, accurately characterizing the cellular landscape surrounding individual plaques.

  3. Imaging

    • Whole-section multichannel fluorescence scanning

  4. Quantitative Analysis
    We have developed fully-automated quantitative analysis for multiplex immunofluorescence:

    • Fully-automated segmentation for microglia (Iba1), neuronal soma (NeuN), and model-relevant pathology markers

    • High-throughput, region-based reporting of microglial burden and morphology-derived state metrics

    • When NeuN is included, contact analysis resolves two interaction topologies:

      • Microglial process-to-neuronal soma contact

      • Microglial soma-to-neuronal soma contact, including quantitative contact area.

Illustration of Biospective's process of collecting brain tissue samples from animal models, performing tissue sectioning, multiplex immunofluorescence staining, whole slide scanning, and quantitative image analysis.

Sample Collection, Preparation, and Shipping Guidelines

We provide comprehensive support to ensure sample integrity and data reliability:

  • Sample Collection: Animals should be perfused with cold PBS and/or 10% neutral-buffered formalin, and the brains should be carefully extracted.

  • Sample Preparation: Brains must be briefly properly fixed in 10% neutral-buffered formalin. 

  • Sample Shipping: Samples must be shipped in PBS with sodium azide.

Why Quantify Microglial Activation and Microglia-Neuron Interactions in Models of Neurological Diseases?

A brief overview of microglial reactivity in neurological disease models and why rigorous quantitative analysis is critical.

Microglia maintain tissue homeostasis through continuous parenchymal surveillance, immune sensing, and tightly regulated phagocytic clearance. In disease, microglia shift into heterogeneous activated states that are regionally patterned and stage-dependent, aligning with selective neuronal vulnerability and the emergence of synaptic and circuit-level dysfunction. These state shifts are mirrored by quantifiable morphological remodeling, including soma hypertrophy and shorter, thicker, and more branched processes.

Functionally, microglia transition from surveillance to an effector state, meaning they shift from primarily sensing and maintaining homeostasis to executing defined response programs that actively modify the local microenvironment, through inflammatory signaling, oxidative mechanisms, complement cascades, and lysosome-coupled phagocytosis.

These structural changes are commonly accompanied by:

  • Increased production and release of pro-inflammatory cytokines and chemokines

  • Reactive oxygen and nitrogen species generation

  • Complement-associated programs

  • Elevated phagolysosomal and phagocytic load

In normal and pathological conditions, microglia directly interacts with neurons for multiple purposes, such as synapse pruning and development (Paolicelli, 2011; Schafer, 2012) and axonal guidance (Squarzoni, 2014). Recently, specialized sites between microglial process and neuronal soma, termed somatic purinergic junction, have been proposed to be a key site for microglial sensing of neuronal health (Cserép, 2020, 2021). These sites were found to be associated with mitochondria on the neuronal side and the purinergic receptor P2Y12R on the microglia side. While the role of somatic junctions in neurodegenerative disorders remains largely unexplored, several lines of evidence suggest they may be altered, including an increase in the frequency of interactions in an acute brain injury model (Cserép, 2020), mitochondrial dysfunction in these conditions, and Biospective’s research study showing  a strong correlation between these interactions and neurodegeneration in a mouse model of Parkinson’s disease.

Together, morphology, effector-pathway engagement, and contact topology provide mechanistically anchored, quantifiable endpoints that link microglial state to neuron-associated engagement within pathology-defined microenvironments, and to downstream neuroinflammatory and neurodegenerative phenotypes. Depending on context, these responses can be adaptive, supporting lesion containment, efficient debris clearance, and circuit support via trophic and synapse-modulatory programs, or maladaptive, sustaining inflammatory signaling and inappropriate phagocytic remodeling or removal of stressed yet viable neuronal elements, thereby accelerating circuit dysfunction and neurodegeneration.

How does Biospective Perform Morphology-based “Activated Microglia” Analysis?

A summary of our methods to classify activated microglia.

Overview of Analysis Methodology

  • Cells are detected and segmented on whole slide images using computer vision and deep-learning approaches.

  • Morphological features such as the soma area, the number of branching point in processes, etc. are then extracted.

  • Based on these features, cells are classified as activated or not and are assigned a continuous activation score by a machine learning model.

  • These metrics are then aggregated by neuroanatomical region-of-interest (ROIs), subject, and group for statistical analysis.

Animated workflow for microglia morphology analysis.

What is the Value of Activated Microglia Analysis?

  • Larger effect size than simply quantifying Iba1 stain density. In multiple contexts, such as in mouse models of Alzheimer’s disease and Parkinson’s disease, Biospective has observed larger effect size in group comparisons using the microglial morphological analysis than measuring the Iba1 stain density. In the context of a preclinical efficacy study, this result means that the same drug effect would be detectable with smaller cohort size.

  • Correlates with translationally relevant clinical metrics. Our microglia morphological analysis has shown strong correlation to translationally relevant clinical metrics, such as motor scores and MRI brain volume, making it a valuable insight into the effects of a putative therapeutics.

How does Biospective Quantify Microglia-Neuron Interactions?

A summary of our quantification methods for cell-to-cell interactions and an illustrative example from a TDP-43 ALS mouse model. 

In order to analyze direct interactions between neurons and microglia, we perform multiplex immunofluorescence including microglial (e.g. Iba1) and neuronal (e.g. NeuN) markers in thin tissue sections. Biospective’s platform automatically identifies and quantifies the properties of each interaction:

  • Size: overlap area, fraction of cellular perimeter covered, etc.

  • Type/Subcellular Localization: soma-soma or microglial-process-to-neuronal soma

Aggregate statistics (e.g. fraction of microglia with process-to-soma interactions) can then be calculated by neuroanatomical regions, subjects, and groups for statistical analysis.

To illustrate our workflow, we have applied this image processing & analysis pipeline to mIF images from the brains of a novel TDP-43 proteinopathy mouse model developed by Biospective. Non-transgenic, wild-type (WT) C57BL/6 mice were unilaterally injected with AAV-hTDP43ΔNLS (or AAV-null as a control) into the substantia nigra pars compacta (SNc). Brains were collected at 6 weeks post-injection.

In this research study, we found that, compared to controls, the AAV-hTDP43ΔNLS group had:

  • Extensive microgliosis, with a strong increase in the Iba1 stain density and the density of activated microglia obtained from the morphological analysis.

  • More microglia-neuron interactions:

    • A larger fraction of neurons had interactions with microglia.

    • Even though the total number of microglia was highly increased, a larger fraction of microglia had interactions with neurons. Interestingly, this metric had stronger statistical significance than pure microglia-based metrics.

    • A large increase in the density of both microglia-process-to-neuronal soma interactions and soma-soma interactions.

  • A change in the nature of the interactions, as the interactions are larger.

  • These interactions appeared to be a direct microglial response to the pathology: the greater the pathological burden was present within individual cells (as measured by hTDP43/pTDP43 markers), the more likely these cells were to have an interaction.

  • These advanced metrics are therefore highly valuable for preclinical therapeutic efficacy studies.  The higher sensitivity means that a smaller effect size would be detected using the same cohort size. In addition, these metrics offer detailed insights into neuronal health, treatment-related microglial responses to pathology, and microglia-neuron interactions.

Interactive Presentation of Our Research Study

In the "Image Interactive" below, you can find results from our microglia-neuron interaction analysis, including high-resolution Multiplex Immunofluorescence tissue sections of brains from our proprietary inducible TDP-43ΔNLS model and control mice.

How to use Our Interactive Viewer
Navigate through the “Image Story” via the left-hand panel or the on-screen arrows. You can pan around high-resolution microscopy images with your mouse, and zoom in/out using the scroll wheel or the +/- controls. The Control Panel (top-right) allows toggling of image channels and segmentation overlays. For the best experience, we recommend switching to full-screen mode. This Interactive Presentation enables you to explore the model’s neuropathology and associated functional deficits in detail, as if looking directly down the microscope.

Microglia-Neuron Interactions in a Novel TDP-43 Mouse Model

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Biospective logo

Authors: Laurent Potvin-Trottier, Lionel Breuillaud, Ashmala Naz, and Barry J. Bedell

Direct interactions between microglia and neurons are thought to play a key role in many processes. In particular, interactions between microglial processes and neuronal soma, termed “somatic purinergic junctions”, have been hypothesized to be a key site for microglial assessment of neuronal health. However, it is still largely unknown how these interactions are affected in neurodegenerative disorders.

To begin to address this question, we measured interactions between microglia and neuronal soma in a proprietary mouse model of TDP-43 proteinopathy developed by Biospective. Non-transgenic, wild-type (WT) C57BL/6 mice were unilaterally injected with an AAV vector expressing human TDP-43ΔNLS or AAV-null as a control into the substantia nigra pars compacta (SNc) and were perfused 6 weeks post-injection. Multiplex immunofluorescence staining of FFPE brain tissue sections was performed for neurons (NeuN), microglia (Iba1), human TDP-43, pTDP-43,and nuclei (DAPI). Our automated image processing & analysis pipeline quantified interactions between microglia and neuronal soma, along with their properties of each direct contact:

  • Size (e.g. overlap area, fraction of cellular perimeter covered, etc.)

  • Type/Subcellular Localization (soma-soma or microglial process to neuronal soma)

In this “Interactive Presentation”, we highlight key metrics quantified in this study:

  • Iba1 stain density

  • Density of activated microglia, obtained from morphological analysis

  • Fraction of neurons with microglial interactions

  • Fraction of neurons with large microglial interactions

  • Fraction of microglia with neuron interactions

  • Density of microglial process-to-neuronal-soma and soma-soma interactions.

  • Relationship between hTDP-43/pTDP-43 fluorescence intensity and probability and size of microglial interactions

To navigate though this Image Story, you can use the arrows and/or the Table of Contents icon in the upper right corner of this panel.

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You can also interact with the microscopy image in the Image Viewer on the right at any time to further explore this high-resolution data. Please feel free to further explore the microscopy images in the viewer.

Mouse Model of TDP-43 Proteinopathy

WT mice were injected with AAV-TDP-43ΔNLS into the left substantia nigra pars compacta (SNc). As can be observed on this microscopy section, mislocalized cytoplasmic expression of hTDP-43 can be observed unilaterally in neurons across the midbrain and the substantia nigra. Punctate localization of hTDP-43, indicative of protein aggregation, is also observed across the affected regions.

Phosphorylated TDP-43 across the Affected Regions

Phosphorylated TDP-43 is found in the regions affected by hTDP-43, indicating the presence of pathological aggregates (arrows) that are the hallmark of neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD).

Low Neuroinflammation in Contralateral Hemisphere

In contrast, the contralateral hemisphere and control mice showed no hTDP-43 and low density of the Iba1 microglial marker. Microglia were observed in low density in their ramified morphology, with extended and fine processes.

Microgliosis

In the ipsilateral hemisphere of injected mice, severe microgliosis was observed in a spatial pattern corresponding to the pathology. We applied our microglial morphology analysis pipeline to quantify the density of activated microglia. Detected microglia are color-coded based on morphological classification, with non-activated microglia shown in lavender and activated microglia in red. Only microglia with nuclei fully in-plane were included in the the detection step and analysis.

In the midbrain and substantia nigra, both the Iba1 stain density and the density of activated microglia showed a highly significant increase in this study with a relatively small sample size (N=8 animals per group). Most cells showed activated microglia morphology, with enlarged soma and shorter and thicker processes.

To learn more about our microglia morphology analysis pipeline, please see our Innovation Presentation on microglia morphology.

Graph showing an increase in Iba1 stain density and activated microglia density in the SNc and midbrain of the disease group.

Microgliosis in the AAV-TDP-43ΔNLS injected group. Increase in Iba1 stain density and activated microglia density in the SNc and midbrain are seen in the disease group. Data are presented as mean ± SEM. Statistical analyses were performed using Welch’s two-tailed t-test. * : p<0.05, ** : p<0.01, *** : p<0.001

Microglia-Neuron Interactions in Control Conditions

To better understand the impact of neurodegenerative disorders on direct interactions between microglia and neuronal soma, we applied our cellular interaction framework in this study. As expected, many interactions can be readily detected in control conditions, although most of the interactions were relatively small compared to the size of the neurons.

Microglia-Neuron Interactions with TDP-43 pathology

In contrast, many more microglia-neuron interactions were observed in the presence of the pathology. To this end, we have quantified the fraction of neurons with microglial interactions, which shows a highly significant increase in the substantia nigra and the midbrain.

Graph showing the fraction of neuron with a microglial interaction increases in the disease group

Increase in microglia-neuron interactions in the AAV-TDP-43ΔNLS injected group. The fraction of neurons with a microglial interaction increases in the ipsilateral SN and midbrain in the disease group. Data are presented as mean ± SEM. Statistical analyses were performed using Welch’s two-tailed t-test. * p<0.05, ** p<0.01, *** p<0.001

Larger Microglia-Neuron Interactions

The increase in microglia-neuron interactions could be driven, in part, by the drastic increase in microglia density. To investigate whether the nature of these interactions changed, we quantified contact properties. For example, the fraction of interactions in which microglia processes partially envelop the neuronal soma (defined here as “large interactions”), was strongly increased in the disease group, consistent with what is observed on this microscopy section. This data shows that not only the quantity of interaction changes, but that the type of the interactions is affected by the pathology.

In addition, the fraction of microglia with neuronal interactions showed a highly significant increase, demonstrating that while more microglia are present in the pathological condition, each microglial cell has a higher probability of interacting with a neuron. Interestingly, this metric showed a stronger statistical significance than other metrics used in this study. In the context of a preclinical therapeutic efficacy study, this result would mean that a smaller effect size could be measured with the same cohort size.

Taken together, these results suggest that the presence of the pathology is affecting the type and quantity of direct microglia-neurons interactions.

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Change in the type of microglia-neuron interactions in the AAV-TDP-43ΔNLS injected group. The fraction of microglia with a neuronal interaction and the fraction of “large” interactions (defined here as covering more than half the perimeter or the area of the neurons) increases in the ipsilateral SN and midbrain in the disease group. Data are presented as mean ± SEM. Statistical analyses were performed using Welch’s two-tailed t-test. * p<0.05, ** p<0.01, *** p<0.001

Interaction Location on Microglia

To further characterize the microglia-neuron interactions, we measured the position of the interactions on the microglia, to classify them as soma-soma or microglial process-to-neuronal soma. In this study, both types of interactions increased similarly by a 3-4 fold ratio.

These results are in contrast to a different study in an α-synuclein mouse model of Parkinson’s disease, where we observed a shift in the type of interactions towards microglial process-to-neuronal soma. In that study, the type of interaction showed a strong correlation to brain atrophy (as measured by MRI) in the disease group. In the current study, we did not observe that shift in the type of interactions, but we also did not observe brain atrophy, further supporting the use of microglia-neuron interaction metrics as translationally relevant endpoints in preclinical therapeutic efficacy studies.

Graph showing the increase in the density of microglial-process-to-neuronal soma (somatic junctions) and soma-soma interactions in the ipsilateral midbrain of the AAV-TDP-43 injected group

Increase in the density of microglial-process-to-neuronal soma (somatic junctions) and soma-soma interactions in the ipsilateral midbrain of the AAV-TDP-43ΔNLS injected group. Data are presented as mean ± SEM. Statistical analyses were performed using Welch’s two-tailed t-test. * p<0.05, ** p<0.01, *** p<0.001

Interactions Associate with Pathology

As direct interactions between microglial processes and neuronal soma are hypothesized to be a key site for sensing of neuronal health, we next investigated the relationship between interactions and the protein pathology (as an inverse proxy for neuronal health). We, therefore, measured the fluorescence intensity of hTDP-43 and pTDP-43 in more than 40,000 individual neurons across the midbrain and substantia nigra of the AAV-TDP-43 injected animals, along with the interaction properties of these neurons. For both hTDP-43 and pTDP-43 markers, the fraction of neurons with interactions increased continuously as a function of the quantity of misfolded protein pathology, albeit with slightly different relationships. In other words, the more pathology is present within the cell, the more likely it is to have a contact with a microglial cell. This relationship can also be observed in this microscopy section, where the density of interaction increases concomitantly with the hTDP-43 intensity.

Graph showing the fraction of neurons with microglial interaction increases with the hTDP-43 and pTDP-43 fluorescence intensity in cells across the midbrain and SN of the AAV-TDP-43 injected animals

Increase in the likelihood of interaction as a function of the protein pathology inside individual neurons. The fraction of neurons with microglial interaction increases with the hTDP-43 and pTDP-43 fluorescence intensity in cells across the midbrain and SN of the AAV-TDP-43 injected animals. Each point is a bin of 3,000 cells averaged in x and y (n=46,371 cells across N=8 animals), with error bars showing the SEM estimate.

By measuring the area of interactions with the microglia, we also observed that as the quantity of intracellular pathology increases, the interactions are, on average, larger. In other words, within the same group, interactions with neurons with elevated pTDP-43 are, on average, approximately twice as large compared to pTDP-43 negative neurons.

Graph showing that the average size of microglial interaction increases with pTDP-43 fluorescence intensity in cells across the midbrain and SN of the AAV-TDP-43 injected animals

Increase in the interaction size as a function of the protein pathology inside individual neurons. The average size of microglial interaction increases with pTDP-43 fluorescence intensity in cells across the midbrain and SN of the AAV-TDP-43ΔNLS injected animals. Each point is a bin of 3,000 cells averaged in x and y (n=46,371 cells across N=8 animals), with error bars showing the SEM estimate.

Taken together, these results are consistent with the hypothesis that direct contacts between microglia and neurons are a specialized site for assessment of neuronal health. They further suggest that these interactions are a direct response of microglia to protein aggregation within individual neurons, and not a simple byproduct of microglial activation.

Summary

In conclusion, we have shown how our fully-automated image processing & analysis pipeline can provide sensitive and informative quantitative metrics of disease. Both the density of activated microglia and the Iba1 stain density were strongly increased in this TDP-43 mouse model. The quantity and type of microglia-neuron interactions were affected by the pathology. Both neurons and microglia were more likely to interact in the disease group compared to controls, and these interactions were larger. The fraction of microglia with interactions showed greater statistical significance than single-channel readouts. The effect of the pathology on the microglia-neuron interactions could also be observed at the cellular level within the disease group, where the more pathologic burden was present within a specific neuron, the more likely it was to interact with microglia.

Spiller et al. have previously shown that, in a transgenic mouse model with regulatable TDP-43 expression (rNLS8 TDP-43ΔNLS), activated microglia play a crucial role in the clearance of hTDP-43 aggregates during recovery from the pathology. Depletion of microglia during the recovery period prevented functional recovery of motor symptoms. Taken together, our findings suggest that microglia-neuron interactions are responding directly to the intraneuronal protein pathology, which may prime microglia for subsequent aggregate clearance. This pathway could be a promising target for the development of novel therapeutics in neurodegenerative diseases.

These advanced metrics are, therefore, highly valuable for preclinical therapeutic efficacy studies.  The higher sensitivity means that the same effect size could be detected using the smaller cohort size. In addition, these metrics offer detailed insights into neuronal health, treatment-related microglial responses to pathology, and microglia-neuron interactions.

Please feel free to further explore the microscopy images in the viewer.

We would be happy to discuss this TDP-43 mouse model, its characterization, and our microglia-neuron interactions analyses if you would like to Contact Us.

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Table of Contents
Control Panel
Section: AAV-TDP-43
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Channels

Image Interactive describing our microglia-neuron analysis, including high-resolution Multiplex Immunofluorescence brain tissue sections, from our AAV-TDP-43ΔNLS mouse model and control mice.

Key Advantages of Biospective's Microglia Staining & Analysis Services:

  • High-sensitivity microglia detection

  • Optional staining with markers of microglial phenotype, including custom antibody/marker staining

  • High-throughput, automated whole slide imaging and neuroanatomical region analysis

  • Unique fully-automated quantitative image analysis

    • Microglial morphology and activation states

    • Microglia-neuron interactions

    • Spatial proximity analysis (e.g. Aβ plaques)

  • Cross-species (mouse, rat) compatibility

  • Complementary services (e.g. tissue & fluid cytokine concentrations measured via immunoassays) 

Selection of Amyloid Plaque Environment Metrics provided by Biospective's Platform

Metric

Units

Description

Stain density

Fraction

Fraction of pixels positive for each stain used in mIF or IHC

Density of morphologically reactive microglia

Counts per mm²

Density of microglia classified to a non-ramified morphology state

Mean microglia activation score

Morphology score

Average morphology-derived activation score of detected microglia in an ROI

Fraction of neurons with microglial contacts

Fraction

Fraction of NeuN+ neurons in direct apposition with Iba1+ microglia

Fraction of microglia–neuron contacts that are process-to-soma

Fraction

Fraction of contacts classified as microglial process-to-neuronal soma topology

This table compares the various quantiative microglial metrics provided by Biospective's platform.

Biospective’s Commitment to Neuroinflammation Research

We have an active research & innovation (R&I) program with a particular emphasis on interrogating the intricate roles that microglia & astrocytes play in neurodegenerative, neuromuscular, and neuroinflammatory diseases. 

At Biospective, we recognize the key role that neuroinflammation plays in neurological diseases, and the value of targeted therapeutic modulation of neuroinflammatory responses. As part of our internal research and innovation efforts, we are actively working to better understand the involvement of neuroinflammation in disease pathogenesis. Our current activities include:

Contact Us CTA

To discuss your study requirements or request a quote for Microglia Staining and Image Analysis services:

FAQs

FAQs

What is meant by direct microglia-neuron interactions?

Microglia–neuron interactions refer to direct contacts between microglial structures and neuronal compartments (Cserep, 2021). While there are many (indirect) interactions through soluble factors, direct interactions are defined as those with physical contact between microglia and neurons. Quantifying these contacts provides a mechanism-relevant readout of neuroimmune engagement at the cellular interface, supporting interpretation of neuron-proximal microglial states and their relationship to neuroinflammatory and neurodegenerative phenotypes.


How do you distinguish microglial process contacts versus microglial soma contacts to neurons?

Biospective classifies contacts by topology using automated segmentation of microglial processes and soma and NeuN-defined neuronal soma. Process-to-soma contacts are defined by apposition of Iba1-positive processes to the neuronal soma boundary. Soma-to-soma contacts are defined by direct apposition of the microglial cell body to the neuronal soma. Outputs include fraction of neurons contacted, contact burden per neuron or per unit tissue area, contact area, and density or fraction of contact types.


How can microglial activation and interactions be studied at scale?

Multiplex immunofluorescence of whole slide images enables simultaneous measurement of microglia identity, microglial state markers, neurons, and model-relevant pathology on the same section while preserving spatial relationships required for morphology-based phenotyping and contact quantification. Biospective’s fully automated analysis pipelines enable consistent extraction of morphology, proximity, state markers, and contact-topology metrics across 100,000s of individual cells across cohorts.


What are complementary biomarkers for microglia activation and neurodegeneration?

Fluid biomarkers can provide minimally invasive measures of neuroaxonal injury and glial activation (e.g. neurofilament light chain and GFAP), alongside cytokine and chemokine panels when inflammation is a key endpoint. Imaging readouts, particularly MRI-derived volumetry and lesion metrics, provide orthogonal measures of tissue integrity that can be associated with microglial state and neuron-associated contact topology.


Which diseases and conditions are associated with microglial reactivity?

Microglial state shifts and microglial responses are prominent across neurodegenerative and neuroinflammatory disorders, including Alzheimer’s disease (AD), tauopathy-related dementias (e.g. FTD/PiD, CTE, CBD, PSP), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), and related proteinopathies and demyelinating disorders. These responses are often spatially patterned, stage-dependent, and aligned with regional susceptibility. For more information, visit Microglia Morphology in ALS., Alzheimer’s Disease & Parkinson’s Disease, Microglia, Astrocytes, and α-Synuclein in Parkinson’s Disease, and Microglia, Astrocytes & Tau in Neurodegenerative Diseases.


Can these endpoints be assessed longitudinally?

Histology endpoints are cross-sectional within a given tissue collection. Longitudinal biology is typically addressed through staged cohorts or serial-sacrifice designs, complemented by in vivo imaging. When available, microglia morphology and contact-topology metrics can be associated with MRI-derived measures (for example regional volumetry) to strengthen pharmacodynamic interpretation in matched cohorts.


References

References

Cserép, C., Pósfai, B., Dénes, Á. Shaping neuronal fate: functional heterogeneity of direct microglia-neuron interactions. Neuron, 109: 222–240, 2021; doi:10.1016/j.neuron.2020.11.007

Cserép, C., Pósfai, B., Lénárt, N., Fekete, R., László, Z. I., Lele, Z., Orsolits, B., Molnár, G., Heindl, S., Schwarcz, A. D., Ujvári, K., Környei, Z., Tóth, K., Szabadits, E., Sperlágh, B., Baranyi, M., Csiba, L., Hortobágyi, T., Maglóczky, Z., Martinecz, B., … Dénes, Á. Microglia monitor and protect neuronal function through specialized somatic purinergic junctions. Science367: 528–537, 2020; doi:10.1126/science.aax6752

Paolicelli, R.C., Bolasco, G., Pagani, F., Maggi, L., Scianni, M., Panzanelli, P., Giustetto, M., Ferreira, T.A., Guiducci, E., Dumas, L., Ragozzino, D., Gross, C.T. Synaptic pruning by microglia is necessary for normal brain development. Science, 333: 1456–1458, 2011; doi:10.1126/science.1202529

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Keywords

Keywords

Accuracy: the closeness of a measured value to a standard or known true value. It is a measure of the correctness of a measurement or the extent to which an estimate represents the actual value. High accuracy indicates that the measurement or prediction is very close to the true value. 

Axonal Injury: damage to the neuronal axon. 

Biomarker: a measurable indicator of a biological state or condition. Biomarkers are often used in medicine and research to detect or monitor the presence, progress, or severity of a disease, as well as to assess the effectiveness of a treatment. 

Brain Atrophy: reduction in volume or thickness of the entire brain or regions of the brain. 

Cytokine: a protein that serves as a signalling molecule among immune system cells. Cytokines are classified into interleukins, interferons, tumour necrosis factors (TNF), chemokines, colony-stimulating factors, and transforming growth factors. Depending on their role in the immune response, cytokines can be categorized as pro-inflammatory or anti-inflammatory. 

Damage Associated Molecular Patterns (DAMPs): endogenous signals released from injured, stressed, or dying cells, such as extracellular ATP or uric acid crystals, that alert the immune system to tissue damage. These signals can activate the inflammasome, promoting inflammatory responses. 

Inflammasome: a cytosolic multiprotein complex that assembles in response to pathogen-associated or damage-associated molecular patterns (PAMPs/DAMPs). It typically consists of a pattern recognition receptor (e.g. NLRP3), the adaptor protein ASC, and pro-caspase-1. Upon activation, it mediates caspase-1-dependent maturation of pro-inflammatory cytokines IL-1β and IL-18 and induces pyroptosis, contributing to innate immune defense and inflammatory pathology. 

Lysosome: a membrane-bound degradative organelle in eukaryotic cells, responsible for digesting lipids, proteins, and other macromolecules. 

Magnetic Resonance Imaging (MRI): a non-invasive imaging modality that uses magnetic fields and radiofrequency (RF) pulses to generate images. 

Microglia: one of the neuroglial cell types present in the brain and spinal cord. Constituting approximately 10-15% of the total cellular population in the brain, microglial cells function as the primary immune cells of the central nervous system. These cells are essential for maintaining homeostasis, clearing cellular debris, and providing critical support functions within the brain. 

Microglia Morphometrics: quantitative measures of microglia morphology, such as cell area, soma perimeter, number of branching points in processes’ skeleton, etc. 

Neurodegeneration: a complex, multifactorial process resulting in the loss of neurons. 

Neuroinflammation: an inflammatory response within the central nervous system (CNS), primarily involving the activation of microglia and astrocytes. This process can be triggered by various factors, including infections, traumatic brain injury, toxic metabolites, and autoimmune diseases. 

Precision: a measure used to assess the accuracy of a predictive model, especially in classification tasks. It refers to the proportion of true positive predictions out of all positive predictions made by the model. In other words, precision tells us how many of the predicted positive instances are actually correct. 

Reactive Microglia: microglia that are responding to or reacting to a particular condition. The name was proposed by Paolicelli et al. (Paolicelli, 2022) in lieu of the discouraged term “activated” microglia, emphasizing that microglia can have many different “reactive states” in health and disease. 

Region of Interest (ROI): a specific subset of data identified within an image. For volumetric analysis the ROIs correspond to neuroanatomical structures and parcellated features. 

Spatiotemporal Pattern: a pattern with both spatial and time components. 

Translational Biomarker: a robust indicator of a biological state or process that is measurable in both animal models and humans.


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