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.

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!

When Visikol was first founded, we were very focused on providing researchers services and products which would enable them to image tissues in 3D. We had initially launched the company as a products and kits company but soon added 3D tissue imaging as a service through a contract research service model to meet the demand from researchers who were interested in the technology but did not want to apply it themselves. However, we always had an eye on the larger applications of tissue clearing and 3D tissue imaging such as applying the approach to the clinic and potentially helping improve the diagnosis of disease and/or helping to determine which treatment would be most effective for a given patient. Over the last six years, we led various research projects in this space and were funded by the NIH as well as the NSF to evaluate the application of our Visikol® HISTO™ tissue clearing technology in the clinic, which presented a few key challenges:

Challenge 1: Defining an Application for Tissue Clearing in the Clinic 

The principal problem which we evaluated was where could tissue clearing and 3D imaging actually add value in the clinic? While the practice of histology has been largely unchanged for a century despite some more recent improvements (e.g., ISH, IHC, slide scanning), the practice works quite well and is highly efficacious in diagnosing most forms of cancer while being quite affordable and high throughput. Therefore, a much more complicated and expensive modality such as tissue clearing would need to be applied to an application where the current practices are insufficient and result in poorer than desired outcomes. In evaluating the space, we determined that the only applications that could benefit were the ones in which diagnosis was poor (i.e., high false negative or high false positive rate) and an improved characterization paradigm could improve outcomes. The second point is interesting as it is possible that while tissue clearing could be “better” (i.e., more accurate, precise, robust) than the current methodology, the patient outcome might be unchanged if there are minimal treatments available. We see this phenomenon with many researchers and companies in the digital pathology space such as with PAPNET decades ago, or its contemporary descendants today who are able to improve the sensitivity and/or accuracy of diagnosis. However, if these tools do not result in a change in clinical outcome such as how a patient is treated as there are limited options, then the tool doesn’t actually change clinical outcomes and wont be adopted despite it being “better.”

Challenge 2: Deploying a Tissue Clearing Approach

In 2012, researchers marveled at the first mainstream tissue clearing work that appeared in Nature and featured the CLARITY approach. However, if you ask these researchers how many of them successfully reproduced the approach in their lab, you will have very few researchers respond positively. This isn’t to knock the CLARITY researchers, but instead is to highlight that tissue clearing and 3D microscopy is very challenging and requires the combination of several disparate and highly complex fields. To successfully image a tissue in 3D requires data science, image analysis, IHC expertise, chemistry expertise and of course advanced microscopy expertise.

As such, its challenging to get tissue clearing with any approach to work consistently as there are so many factors at play (e.g., fixation, antibody lot, tissue quality, laser life) which effect its performance. From our experience, we struggled with dropping tissue clearing into clinical workflows, as the tissue preparation process varied so widely from facility to facility, and validating the approach in a decentralized manner (I.e., at local hospitals) or at a centralized facility with multiple different sites was very challenging and inconsistent. For example, the antibody concentration and incubation time is dependent upon the volume and shape of a tissue and slight variations can have a significant impact on data outputs which greatly reduces the sensitivity of the approach.

Future Steps 

Ultimately, our work in developing clinical applications for tissue clearing has been unsuccessful to date at Visikol and we were not able to meet the high bar required to develop a clinical diagnostic tool despite significant effort. While we have shown the ability to improve the detection of tumor cells in the context of metastatic melanoma and potentially reduce false negatives in the clinic and improve patient outcomes, we have been unable to implement such an approach in the clinic due to the challenges described above. We found that the variation in samples and the consistency of the approach reduced any added sensitivity gained in comparison to traditional 2D histology and thus the approach added marginal value while adding significant cost and complexity. However, this does not mean that the field is a dead end. Instead, we see 3D microscopy as potentially yet bearing fruit but with other approaches, such as in conjugation with non-label based imaging modalities and using complex image analysis approaches to reduce noise and improve sensitivity.

If you are interested in learning more about tissue clearing, download our Tissue Clearing EBook, check our tissue clearing overview page, or watch our tissue clearing webinar.

Be sure to reach out to a member of our team if you’re interested in discussing your latest project!

When we first started Visikol in 2016, the application of tissue clearing to improving clinical diagnostics was of course a huge interest and a way we felt that we could have a substantial impact on patient outcomes. The gold standard approach for many decades has been to characterize tissues such as cancer biopsies using ultra-thin two-dimensional slices that are visualized by a pathologist in a human driven workflow. We felt that of course an approach that could image tissues in 3D instead of 2D would be better, as would a computer vs a human in characterizing a tissue. However, 3D tissue imaging by way of tissue clearing and digital pathology in general have been slow to be adopted in the clinical space which begs the question of ‘Why?’

Why has adoption taken so long?

Everyone wants to blame pathologists for the slow adoption of digital pathology and advanced tissue imaging such as multiplex and/or 3D tissue imaging in the clinic. These are of course the folks working in the basement of a hospital with the microscope on their desk that are just too old-fashioned to adopt new technologies. The truth though is that these pathologists are not the saboteurs of these new technologies, and the real reason for this slow adoption is much more complicated than this.

What if I told you that digital pathology, machine learning, neural networks and slide scanning had already been launched in the clinic 30 years ago – would you believe me? This is in fact true, and if you want to read back to the early 1990’s, PAPNET was introduced to improve the detection of cervical cancer on pap smears. To quote their explanation of the technology PAPNET was “A system that employs automated image analyzer technology coupled to neural network computer technology to create a system that by itself can capture images of cells and cell groups and systematically analyze how far the images diverge from the normal state toward an abnormal one of dysplasia or neoplasia.”

This was an advanced approach for digital characterization that had aimed to replace the pathologist with a digital approach. Technologically, PAPNET was very successful and it can be argued that at the time PAPNET was in many ways better than traditional pathological characterization. However, you have likely never heard of PAPNET, and today pap smears and the majority of tissues in the clinic are still evaluated by a pathologist, so what happened? Most technologists and innovators tend to assume that newer technologies like PAPNET or the panoply of digital pathology companies today will be successful as an axiom because more data and newer technologies must be better than decades old approaches. However, in the clinical space what is most important is first what impact a technology has on clinical outcomes and second how much that technology costs.

For example, in the case of breast cancer we are able to better visualize micro-metastases, as well as isolated tumor cells, in sentinel lymph node biopsies using tissue clearing and 3D tissue imaging. However, this information actually has no impact on improving patient care, as the information would not change the current paradigm of care and what treatments that patient receives. This is because we know in the context of breast cancer that we need to have a specific disease burden in order to justify changing a treatment regimen and the improved sensitivity of 3D tissue imaging does not help in this regard compared to traditional approaches and is much more expensive.

The second limitation is in regard to cost, which is a point that many scientists tend to ignore as the exercise of putting a financial figure on life itself appears overly morbid and is frankly uncomfortable, as we tend to like to think that life could not possibly have a value associated with it. However, we live in a world with a finite amount of resources, and we, as a society, need to determine the financial impact of the healthcare decisions we make and allocate our finite resources appropriately. For example, we, as a society, would of course agree that a $1M lifesaving treatment for a toddler is worth it provided that they are able to then live a complete and productive life, but we would likely not agree that a treatment with a similar price for a cancer patient that provides them with an additional month to live would be financially worth it. This is, of course, not a fun thing to say out loud but is the tough decision that is left to healthcare leaders. As healthcare systems, we tend to value life in quality-adjusted life-years (QALYs) and depending on what country you live in these will have an associated cost, such as let’s say $100,000 per QALY.

In the case of PAPNET switching every single pap smear evaluation to this new approach adds $100M in cost and only saves a few hundred QALYs, we would agree that the technology does not have a sufficient ROI and that these additional costs could be better applied to other healthcare solutions.

Ultimately, the failure of PAPNET was due to both cost and clinical outcome issues as well as the traditional processes being moderately adapted to even further improve themselves. What can be learnt from this is that the adoption of these technologies is not at all a technological problem or a problem with saboteurs, but instead a market fit problem, whereas (wherein?) the technologies just simply don’t add value compared to traditional approaches. As a society where people use their iPhone 13’s instead of an iPhone 8, which is functionally the same thing, I think this reality is awfully tough to understand which is why we see so many companies go after this problem and so many of them be unsuccessful.

Where do advanced imaging and digital pathology technologies fit in?

Today, we largely see advanced tissue imaging tools and digital pathology tools being used in the preclinical space for basic research, translational research, biomarker discovery and a wide range of other research questions. In these applications, these technologies allow researchers to answer questions that they would otherwise not be able to answer and also to accelerate their drug discovery and development programs. In the future, we see these technologies being adopted into the clinic in two distinct areas- niche applications and clinical applications involving complex patient-specific treatments.

While the diagnosis of breast cancer SLNB’s might not be improved by the use of tissue clearing, what about melanoma where the threshold for lymph node positivity is far lower? In this niche application, it is possible that an advanced technology like 3D tissue imaging might add clinical outcome value, as the false negative rate is quite high with traditional approaches and not receiving appropriate care has significant negative outcomes as metastatic melanoma is such a virulent disease. At Visikol, over the last two years we have been working through an NIH funded project to evaluate this application and have so far shown great preliminary data evaluating this approach. Therefore, it is possible there are niche applications for where these technologies can fit in and add value but we must give credit to the traditional approaches and admit they are quite efficacious for most applications.

The second field in which they can add tremendous value is in the field of precision medicine and immuno-oncology, where we have a large number (potentially infinite) of treatment solutions for a patient and we need to know as much about the patient as possible and thus this information could only be gained from advanced imaging and image analysis modalities. This is a field that is rapidly growing and while we don’t yet have an immediate need for such diagnostics as the treatments are not quite there yet, this is a field that is rapidly growing and might in the near future require such a tissue visualization approach.

Since the inception of tissue clearing, Visikol has been on the forefront of the field and continues to be a leader in 3D tissue imaging, multiplex tissue imaging and digital pathology.  If you are interested in learning more about how digital pathology or advanced imaging can fit into your research, please reach out to Visikol and our team.

Visikol offers a wide variety of cell-based assays, ranging from 2D to 3D and many of these assays draw on Visikol’s expertise in imaging and image analysis. Most live cell imaging assays are performed in 2D such as wound healing and calcium flux assays, however live cell imaging may be appropriate for some 3D assays as well. In particular, 3D invasion assays (angiogenesis assays) and immune cell infiltration assays can be suitable candidates for live cell imaging, but something like a cell viability assay, while able to be evaluated using live cell imaging may ultimately be more suited for a different approach. So, when is an appropriate time to use live cell imaging verses a more traditional fixed-cell approach?

Living Cell Imaging vs. Fixing Cells

The choice of live cell imaging verses fixing cells at set time points is entirely dependent on the type of study being done and the questions being asked. In general, live cell imaging is advantageous when the experiment requires multiple time points, and the desired outputs can’t be adequately determined with one final endpoint or other non-destructive endpoints. For instance, in a wound healing assay (scratch wound assay) to determine how the cells are moving, images need to be taken of the same scratch at the beginning and the end of the study at a minimum, so that differences in how “healed” the wound is can be determined. However, doing the assay this way only gives a snapshot into how the wound healing took place. To get a better idea of what is happening to the cells, and to be able to better quantify things like cellular velocity and cell proliferation, it can become necessary to increase the number of timepoints, where instead of imaging only at 0 hours and 24 hours, a researcher may instead opt to image at 0, 6, 12, 18 and 24 hours. While it is possible to have someone take a sample in and out of an imager for each time point, it becomes burdensome when time points occur outside of standard work hours or when the time points are close together, this is where a live-cell imaging system can come in handy. Live cell imaging systems incorporate incubation, which allows for temperature, CO₂, and humidity levels to be maintained and thus cell cultures can be imaged for days or even weeks.

Live Cell Imaging Considerations

While live cell imaging is useful in some instances, it can become cumbersome, as live cell imaging can take up a lot more microscope time and become cost prohibitive, both in terms of imager time and in the costs associated with the generation of large amounts of data (i.e. storage and analysis). Thus, for longer term experiments it can become necessary to weigh the pros and cons of live cell imaging. For instance, a four-week long study that wants to look at viability over time could be done with a viability dye and live cell imaging, but it would likely be more cost effective to collect supernatant samples over the four weeks and evaluate them for the release of cytotoxicity markers.

Another thing to consider is the target of interest. While there are many live cell dyes on the market that can allow for the visualization of the nucleus, organelles, cytoskeleton etc. without causing harm to the cells themselves this list is not all inclusive and some of the dyes do not hold-up well for long term imaging. An alternative way to visualize targets of interest in cells is through transfection of the cells so that the cells produce a fluorescent version of the target of interest. This can be a useful method, but is often time consuming to achieve stable cell lines for use in a study. Before moving forward with these methods, it should be considered whether fixing cells and using immunofluorescent labeling could accomplish the same outcomes.

Ultimately it is useful to take a step back and think about whether a study truly requires live cell imaging because while it can be key to understanding certain processes it can add significant costs to a study when relevant answers could be obtained with more traditional and less expensive fixed end points. If you would like to learn more about whether live cell imaging is right for you or if you are interested in any of the assays Visikol offers, please reach out.

Creating 3D representative figures from confocal microscopy can be extremely useful for illustrating holistic views for any subject of research. Visikol is constantly expanding its 3D models and currently offers organoids, microtissues, and spheroids as part of its drug discovery services. This article will walk through some routine tools used to create 3D figures of a Retinal Organoid, used for fibrosis assays.

Preparing a Hyperstack:

A “Hyperstack” in is a multi-channel/multi-slice image, much like an RGB image, but the contrast of each channel can be altered. To create a hyperstack, you must note the number of channels and the z-steps. This example is an organized 16-bit, 4-channel image with 44 z-steps. Since stack order increases by channel, then z-step, choose the default option in drop down menu of the Stack to Hyperstack Function [Image–>Hyperstacks].

Update Image Properties to Utilize Orthogonal View:

The Orthogonal Views tool [Image–>Stacks] is the quickest way to create transverse and coronal views of data.  Unless you’re using cubic voxels, your data most likely will be “squished”. This is because z-steps are usually at a lower resolution than the pixel resolution for microscopy. To address this, you simply need to change the z-step size via the image Properties menu [Image–>Properties]. After re-running the Orthogonal Views function, the aspect ratio of the voxels will be stretched in the z-direction (Figure 2).  By eliminating the restriction of working within a single view, we can distinguish patterns and features that we would otherwise miss.

Apply Lookup Table and Utilize 3D Viewer

One of the most challenging obstacles when dealing with microscopy data is confocal bleeding and autofluorescence artifacts. This residual signal needs to be removed or the rendering will include a halo that could lead to misinterpretation. There are many ways to tackle this, such as advanced filters and mask applications, but for the novice imageJ user you’re only a few clicks away!

For this method, lower end of the histogram is clipped, and a new lookup table was created each channel via the Brightness & Contrast tool [Image–>Adjust]. Although this tool is finicky, the procedure is extremely simple. All you need to do is adjust the Minimum slider to remove the halo towards the top of the stack, and optionally adjust the Maximum slider to improve contrast for the entire stack (Figure 3).  This may take a few attempts, and it is suggested you take note of the upper and lower thresholds at each channel. Once the image is to your liking, make this a permanent change by converting the image type (Image–>Type–>8bit). ImageJ will snip above and below the thresholds indicated on each histogram and stretch the remaining values within 8-bits. The dataset is now ready for volume rendering (Figure 4).

The 3D capabilities supported in imageJ are growing as the demand for 3D data analysis increases in various areas of research.  ImageJ is one of many tools Visikol uses on its mission to refine existing and develop new 3D assays. If you have any questions about the developing a custom 3D pipeline for your path to drug discovery, do not hesitate to contact Visikol today.

Figure 4: User input for the 3D Viewer [Plugins], and the resulting volume rendering of the hyperstack.

Visikol has added an additional ImageXpress® Micro Confocal High-Content Imaging System from Molecular Devices to its robust imaging capabilities and now has three high-content confocal systems, several fluorescent slide scanners, a Bruker MuVi SPIM Light Sheet microscope, and various other imagers. Just over two months since its acquisition by BICO, Visikol continues to grow its laboratory capacity, as well as its team. With another imager, Visikol will be better suited to aid researchers in their drug discovery efforts and is now able to support even larger drug discovery campaigns that leverage advanced high-content imaging.

The ImageXpress Micro Confocal system is a high-content imager capable of switching between widefield and confocal imaging and can capture whole organisms, thick tissues, 2D and 3D models, and cellular or intracellular events. The ImageXpress Micro Confocal system is also capable of taking high-quality images, creating multiple image models, customized image acquisition, and accelerated analysis speed.

“We are excited to add another Molecular Devices imager to our lab. As we continue to grow both our advanced cell culture and advanced imaging services, we  rely heavily on equipment like the ImageXpress to increase efficiency and improve the data that we provide our clients,” said CEO Michael Johnson, PhD.

The ImageXpress Micro Confocal system will support Visikol’s end-to-end 3D advanced cell culture services and provide the team with the ability to image both simple 2D monolayer models as well as advanced 3D cell culture models. As a company, Visikol prides itself on its ability to leverage advanced cell culture models to provide its clients with the most relevant in vitro assays to address their research questions. At the heart of these assays is the use of advanced imaging modalities, like those found in the ImageXpress high-content imaging portfolio, to generate enormous quantities of imaging data and turn it into actionable insights using Visikol’s suite of image analysis software.

If you are interested in learning more about Visikol’s imaging capabilities and working on your next high content assay, please click here to contact our team!

Visikol Associate Scientist Kevin Dennis using the ImageXpress® Micro Confocal High-Content Imaging System from from Molecular Devices.