What Is Histology?
Histology is the study of biological tissues at the microscopic level. It is essential for understanding differences between healthy and diseased tissue structure and function, which can inform clinical evaluation and drug discovery.
Traditional histological workflows are as follows:
1. Tissue fixation
2. Embedding, sectioning (5–10 µm)
3. Staining (e.g., H&E, IHC)
4. Light microscopy
This workflow has powered biomedical research and diagnostics for over a century. These classical approaches do have a few key limitations: they slice 3D tissues into 2D sections, risking sampling bias, and traditionally rely heavily on subjective human interpretation.
Let’s dive a little deeper into histological workflows below:
Key Steps in Histological Workflow
- Fixation
Stabilizes and preserves tissue structure by cross-linking proteins, most commonly using formalin, preventing decay and maintaining morphology. - Embedding & Sectioning
Tissues are embedded in paraffin or resin, then sliced thinly (usually ~5–10 µm) with a microtome, enabling light to pass through the sample for microscopy. The sections are then adhered to glass slides prior to labeling and imaging. - Staining
Essential for highlighting cellular and tissue structures by enhancing contrast and detail. Common examples:- Immunohistochemistry (IHC): uses antibodies to detect specific antigens in a tissue, allowing for labeling of specific markers of interest. IHC can be colorimetric or fluorescent depending on specific applications.
- Hematoxylin & Eosin (H&E): stains nuclei blue-purple and cytoplasm and extracellular matrix pink.
- Masson’s trichrome: A combination of multiple stains that allows for visualization of nuclei (black), cytoplasm and muscle (red) and collagen (blue).
- Van Gieson’s stain: differentiates collagen (red) from muscle/cytoplasm (yellow.) Can be paired with hematoxylin staining to visualize nuclei.
- Imaging
Traditionally done via light microscopy; today, includes fluorescence (IF), brightfield, and confocal microscopy for 3D imaging.
Visikol has taken this traditional method and enhanced it with a modern pipeline:
Visikol HISTO Tissue‑Clearing for 3D Imaging
We have developed a user-friendly, solvent-based clearing method called HISTO. HISTO renders tissues transparent without harsh lipid extraction, and the result is fully reversible. Utilizing this method preserves cellular morphology, is compatible with fluorescent IHC and its reversibility enables downstream 2D H&E or IHC validation.
NCI-H2170 spheroids approx 250 um in diameter labeled with nuclear stain. Left is in PBS and right is the same spheroid after clearing with Visikol HISTO-M.
Reagent Options:
- HISTO-1 (RI ~1.50): sufficient for thin samples (≤500 µm, thin brain slices.)
- HISTO-1 + HISTO-2 (RI up to ~1.53): optimal for thicker tissue (>1 mm)—e.g. whole mouse brains.
- HISTO-M (RI ~1.48): tailored for high‑throughput 3D cell cultures (organoids, spheroids) ≤1 mm.
Advantages:
- rapid clearing,
- compatibility with fluorescent proteins/immunolabeling
- comprehensive and repeatable protocols
- no need for special equipment
For organoid screens, HISTO-M simplifies workflows in 96‑well formats with protocols and pre-made buffers.
Click here to download our comprehensive guidebook. Additionally, there are tutorials and resources to help labs adopt tissue clearing methods easily.
Advanced Digital Histology & Analysis
We also offer advanced image analysis services and machine learning–driven classification of histology images, consisting of services that cover supervised/unsupervised models to automate cell counts and tissue phenotype analysis.
Our advanced image analysis services use slide digitization (brightfield or fluorescence) using a Zeiss Axioscan 7, and output images via cloud or USB. Our platform applies quantitative analysis across H&E or IHC slides, such as segmentation, tumor area estimation, fibrosis quantification, etc.
Another service offered is cell-counting with marker colocalization, subpopulation classification, and spatial analysis (e.g., T-cell density in tumors.) Machine-learning classification, such as using supervised (CNNs, Random Forests) or unsupervised models, is used to identify staining patterns or morphological phenotypes.
Why Does This Matter?
- Biomedical Research: Allows for advanced study of disease mechanisms, tissue development, and therapeutic impacts.
- 3D Imaging: The ability to move beyond 2D slices and reveal the parts of tissue architecture previously obscured by the 2D method.
- Quantitative Analysis: Powerful digital tools designed to improve throughput and remove bias from slide interpretation.
Why Visikol’s Pipeline Matters
- Comprehensive 3D insights ➝ 2D validation: Explore tissue architecture intact and confirm key observations at the histological level.
- Quantitative, objective data: Algorithms minimize pathologist bias, improve reproducibility, and deliver numeric rigor.
- Scalable across models: From organoids to whole organs, Visikol offers protocols, kits, and services tailored to your sample type.
Bringing It All Together
Histology remains a cornerstone of biological and medical science, and its value to the scientific community is immeasurable. Our platform is meant to enhance this foundation by integrating innovative tissue clearing reagents, robust digital analysis, and Machine learning-driven insights.
We’ve created a versatile toolkit that bridges traditional microscopy with next-generation imaging, with the goal of empowering researchers to explore complexity across two and three dimensions.
Our aim by blending classical techniques with our cutting-edge clearing and analytics is for histology to evolve into a powerful multidisciplinary tool. Reach out to a member of our team to get started today!