Six Things to Consider Before Adopting 3D Cell Culture Models into Your Drug Discovery Pipeline

In an effort to improve the translational gap between in vitro and in vivo studies, researchers are shifting their focus towards advanced 3D cell culture models (e.g. spheroids, organoids, microtissues) that more accurately mimic the in vivo microenvironment compared to traditional 2D monolayer models. However, the field of 3D cell culture models is relatively nascent as is its application in large scale drug discovery efforts. Many companies are just beginning the process of evaluating and implementing assays that leverage the improved predictive capability of these models. The first thing that any researcher will notice about this space is that there is an incredible amount of diversity in the biology of these models, how they are cultured, how these models are characterized and ultimately the advantages and disadvantages of each model. It is also interesting to note that because of how new the field is there is not yet industry consensus about the best types of models for specific research questions and applications.

To begin adopting 3D cell cultures into your research workflow, the first thing that you need to understand is that these models are not a replacement for 2D cell culture models. While, 2D cell culture models are purported to not mimic the in vivo microenvironment as well as 3D cell culture models, these models still have their place in the drug discovery workflow as they are inexpensive and ultra-high-throughput. 3D cell culture models by their nature will always be significantly more expensive than less selective 2D cell culture models and thus it is suggested that 3D models be used to supplement and enhance 2D models and not replace them.

To best develop a plan for 3D cell culture adoption, the first step is to begin at the end and to determine the type of data that you need to acquire from your model and what your research question is.

1. Characterization Method:

One of the challenges with 3D cell culture models is that currently the techniques used for characterization were originally developed for 2D cell culture models and these tools often provide limited or even misleading insights about these models. 

Dissolution Assays:

The simplest assays that a researcher can implement using 3D cell culture models are dissolution-based assays (e.g. total ATP) in which a fluorescent readout gives a singular data-point for each well in a plate. While these assays are ultra-high-throughput, inexpensive and do not require special equipment, they only provide one data point for each model and do not leverage the spatial information that makes 3D cell culture models intrinsically more valuable.

Size and Number of 3D Cell Culture Models:

Within some 3D cell culture systems, multiple models can be generated per well and in simple assays the total number of 3D cell culture models is used as a singular data point per well. Similarly, simple image analysis algorithms can be used to measure the diameter of 3D cell culture models and to use this as a singular end point as well.

Histological Sectioning:

Some questions will require that image-based end-points are acquired and these models can be extracted from a well plate and processed using conventional histology (e.g. H&E and IHC). However, one of the challenges with this approach is that removing these models from a well plate and performing histological processing is a tedious and cumbersome process that is ultra-low-throughput and expensive.

Widefield Imaging:

High content imagers can be used to acquire image-based end points from 3D cell culture models in a high-throughput setting. However, the major limitation with wide-field imaging in a 3D cell culture model context is that these imagers are designed for 2D constructs and thus have a tendency to poorly represent the complex nature of 3D cell culture models. The best analogy for describing this limitation is to consider the Earth as a 3D cell culture model that you want to study and simply taking a picture of the earth from the moon. While you would be able to characterize half of the surface of the Earth with this approach, you would know nothing about the majority of the earth, i.e. its interior. This is especially problematic with 3D cell culture models as the periphery is most exposed to a compound of interest and exhibits vastly different characteristics than the interior of the model.

Confocal Imaging:

Confocal microscopy allows for optical sectioning throughout tissues and for 3D data sets to be acquired which enables complete 3D cell culture characterization. Through confocal microscopy, every single cell within a 3D cell culture model can be imaged and characterized for multiple biomarkers through using multiple illumination channels. A major limitation with confocal microscopy in this context is that if you are acquiring multiple images from a single well, the imaging and processing is going to take significantly longer than widefield assays and thus your throughput is much lower with this improved data density. The other problem is that without the use of a tissue clearing technique like Visikol® HISTO-M™, optical attenuation limits confocal microscopy to only characterizing the periphery of these models instead of their entirety.

The reason that it is so important to intimately understand your desired endpoint is that each type of characterization assay requires different requirements for automation, well plates, labeling and data processing. There is a balance between acquiring lots of data and maintaining throughput where improved throughput will ultimately reduce data generation and thus it is important to understand the minimum amount of data per well required to answer your research question.

2. Cell Culturing Technique:

To generate 3D cell culture models there are a wide range of technologies available, but these techniques can be primarily characterized into scaffold-based techniques and non-scaffold techniques. Scaffold techniques such as Matrigel allow for cells to form 3D constructs within a matrix that mimics the in vivo extracellular matrix. For the non-scaffold approach, there are a wide-range of techniques such as the hanging drop approach, the n3D “bioprinting” approach and ultra-low-attachment (ULA) U bottom plates.  Each one of these techniques has its own advantages and disadvantages in regards to cost, ease-of-use and performance and therefore there is not a specific cell culturing technique that is ideal for all applications. For example, while Corning ULA plates make cell culturing very easy and inexpensive, the round bottom of the plates can cause imaging artifacts with LED-based imaging systems for confocal imaging and are incompatible with some objectives such as the inverted watering dipping objectives on the Perkin Elmer Opera Phenix.

3. Model Type and Relevancy:

For some types of cells it is relatively easy to get them to aggregate and form 3D cell cultures and for others it can be more challenging. However, a major question that must be addressed is whether or not the biology you have chosen to generate in 3D in vitro assays is actually indicative of your in vivo target microenvironment. Therefore, much thought should be put into the types of cells that you use for your assay and the overall composition of your 3D cell culture model. Additionally, a validation plan needs to be developed to demonstrate that the model you have created replicates the functionality of an in vivo system. Some assays will benefit from highly complex 3D cell culture models with multiple cell types with well characterized functionality data while for other applications very simple 3D cell culture models might be able to give you the type on data that you want to collect. Ultimately, the model which you develop and its utility will be dependent upon your specific research question.

4. Throughput and Cost

Along the way of identifying an ideal model, cell culturing technique and characterization approach, an eye should always be kept on throughput and cost. The main rationale for this is that the throughput will determine the type of automation equipment that is required as well as the overall cost of the study. For example, if your research question requires a 500 um model with three cell types and the colocalization of four different markers in all cells with 5,000 compounds, then this assay will require dramatically different equipment than a similar assay with only an ATP total fluorescence readout. The reason for this is that the first assay would require the use of a confocal imager and would likely require one with multiple detectors for simultaneous marker imaging for accelerated throughput while the second assay would only require a simple plate reader and a robot to pipette and quickly load plates.

5. Data Processing

There is a common misconception in the drug discovery context that more data is always better as more data comes at the expense of reducing throughput or increasing study cost. It is important to always consider the minimum amount of data required to answer a research question when considering imaging-based end-points (e.g. sectioning, wide-field microscopy, confocal microscopy) as doubling imaging magnification from 10X to 20X can result in an 8 fold increase in data as well as a >8 fold increase in image acquisition time.

6. Internalize or Outsource

The last consideration is whether you should internalize 3D cell culture model generation and their analysis in-house or if you should outsource this process or components of it to third parties. There are many companies such as InSphero, Stemonix, TARA Biosystems and Mattek that have already developed validated 3D cell culture models that you can purchase and there are several companies such as Visikol that already offer full-service 3D cell culture assay services. Like all outsourcing, this choice comes down to resources and competencies where 3D cell culture assays require a deep set of diverse competencies and resources that you may not have access to.

Visikol Society of Toxicology Posters

In case you missed them - below are our posters and abstracts from SOT 2018 in San Antonio: 

3D Histological Characterization of Precision-Cut Lung Slices for Inhalation Studies

Inhalation studies for allergens and pathogens typically rely upon flow cytometry which provides quantitative analysis of cell proteins associated with immune cells. However, flow cytometry does not provide information on the migration of these cells within the lung and is highly limited in describing the complexities of the lung. Due to these limitations, researchers have begun adopting precision cut lung slices (PCLS) as an in vitro tool as they are able to provide significantly improved in vivo relevancy. However, one of the current limitations in using PCLS models is that they are approx. 250 µm in thickness, and are too thick to image in their entirety using confocal microscopy. Therefore, it is challenging to capture data from these tissues past a few cell layers due to optical attenuation. Through this work, a rapid tissue clearing technique was applied to PCLS models in a high-throughput/well-plate-compatible format to enable whole mount 3D imaging of the entire PCLS model with confocal microscopy. It was shown that this tissue clearing approach was compatible with several commonly used labels (e.g. DAPI, CD68, tomato lectin, PDGFR, alpha-smooth muscle actin) and that uniform labeling as well as complete tissue characterization could be accomplished.


Optical Projection Tomography with a Tissue-Clearing Agent for Developmental and Reproductive Toxicology Studies

Developmental and reproductive toxicology (DART) testing is one of the most expensive and time-consuming stages in determining the toxicological profile of new chemical entities. Within DART studies, gross morphological evaluation of fetal animal skeletons for developmental abnormalities presents a major bottleneck. Current processing techniques involve digestion of soft tissue using a strong base, followed by qualitative assessment of the remaining skeletal tissue by a pathologist. Micro computed tomography (microCT) has been proposed as a non-destructive image-based alternative. Such methods eliminate the need for extensive tissue processing and can be paired with quantitative analysis algorithms. However, due to the significant capital and operational expenses required for microCT imaging, this approach has yet to gain widespread traction. Here, we propose a cost effective optical imaging alternative. A novel tissue clearing agent was used in 1-week old rats to temporarily render soft tissue optically transparent. Alizarin red was used to stain skeletal tissues. High dynamic range (HDR) optical trans-illumination images were then acquired with a low-cost optical-CT imaging system and rendered as 3D images of skeletal structure. HDR-based optical-CT imaging of chemically cleared tissues can rapidly generate high resolution (50–250 um) reconstructions of whole skeletons. This study demonstrates that the combination of tissue clearing, optical imaging, and novel reconstruction algorithms may present a new paradigm for low-cost, high-throughput evaluation of tissues in DART testing.


Improved Characterization of Compound Toxicity through the Application of a Tissue-Clearing Technique to 3D In Vitro Model Screening

The use of in vitro three-dimensional (3D) cell cultures has increased dramatically for drug screening as 3D culture models more accurately mimic the in vivo environment compared to traditional monolayer cultures. However, unlike for traditional cell culture, imaging analysis of 3D cultures is limited due to the thickness of 3D cell cultures (typically >100 μm) which causes light scattering, limiting imaging to the surface-layer cells of the 3D culture. This limitation prevents the complete characterization of the cell population within whole-mount 3D cultures. Furthermore, this technical limitation introduces an unavoidable sampling bias in imaging analysis, since only the exterior cells can be imaged where concentrations of nutrients, oxygen, and drug compound are greatest. It was sought to solve the problem of 3D cell culture opacity by employing an optical clearing agent designed specifically for these tissues. Here, we combine HepG2 Spheroids and the Visikol® HISTO-M™ clearing agent to illustrate the effect of tissue clearing on high content confocal screening of steatosis and cytotoxicity induced by several known hepatotoxic agents. It was demonstrated that the addition of tissue clearing allowed for a 3-fold increase in cells detected, the ability to characterize dose response as a function of tissue depth and an increase in dose response sensitivity.

in vitro.jpg

Imaging Whole Rodent Brains with Tissue Clearing

Over the last few years we have heard from hundreds of researchers that have said that they have tried tissue clearing and “it did not work.” While this feedback is common from researchers, we have learned that these failures are typically due to a couple of common misconceptions about tissue clearing. To dispel these myths, we will start off by saying that all tissue clearing techniques work, but each technique has its own specific advantages, disadvantages and special considerations. Additionally, some tissue clearing techniques are more challenging than others and slight deviations on the protocol can result in poor results.

Most researchers who try tissue clearing read the original CLARITY paper in 2013 or have seen the recent uDISCO paper on whole mouse tissue clearing. In these publications very large pieces of tissue are cleared and imaged intact in 3D. While these images and videos are incredibly impressive, one must ask themselves if they really need to process and image tissues this large for their specific research question. The reason why this is so important to consider is that imaging tissues deeper than 2 mm dramatically increases the complexity of imaging as well as the time required for tissue processing. Imaging past 2 mm into tissues requires the use of expensive dipping objectives and/or a light sheet microscope.

From our experience, the most common error that researchers make is that they try to replicate these whole tissue imaging protocols right away before understanding the fundamentals of 3D microscopy, tissue labeling or tissue clearing. This often leads a researcher into a situation where they achieve poor results and do not know how to trouble shoot their problem.

Why are we imaging brains in their entirety anyway?

One of the things we commonly forget in our pursuit of cutting edge research is that the original research question we set out for. For example, if our goal is to map the vasculature in an entire mouse brain and better understand tortuosity, why do we need to image the mouse brain in 3D as one whole piece instead of a few large pieces? The reason why this question is very important is that labeling and clearing a whole mouse brain is very expensive, time consuming and challenging. If instead we were to split the mouse brain into a few smaller pieces, we would exponentially decrease the amount of time required to process the tissue for labeling as well as clearing. The pieces could be digitally reconstructed into one tissue later. Additionally, reducing the individual piece sizes to less than 2 mm in thickness would allow the brain to be imaged on just about any 3D microscopy system where whole brains require specialized optics and/or uncommon light sheet microscopy.

To adopt tissue clearing into your workflow, we suggest starting small and beginning with thin sections of tissue and working your way up sequentially as you demonstrate uniform labeling in each thickness of tissue. It is uncommon for your research question to require tissue pieces larger than 2 mm in thickness and we always suggest minimizing overall tissue thickness as it will reduce overall time and cost. The other important consideration to keep in mind is that the current literature on tissue clearing typically describe each clearing technique for a specific application. Protocols for each technique will vary greatly by tissue type and thus it is very important to understand the principles of each technique and how to optimize a protocol for your specific research question. With our Visikol HISTO tissue clearing technique we have embodied this protocol customization in our protocol builder tool.


Considerations for Outsourcing Key Aspects of the Drug Discovery Process

Regardless of the industry, the outsourcing of specific workflows within a company’s operations can offer key competitive advantages through allowing a company to focus on its core competencies where it derives the highest return on investment. This fundamental business principle was originally distilled into Wealth of Nations by Adam Smith 250 years ago and holds true on a macro as well as micro scale today. Focusing efforts on a core set of competencies allows for improvements in cost and business efficiencies that lead to a reduction in overhead and increased operational control over  top priorities. Specific to the pharmaceutical and biotechnology fields, the outsourcing sectors have matured over the last two decades in terms of contract drug formulation and development, contract testing, contract manufacturing and packaging.

While commoditized assays have migrated from pharmaceutical and biotechnology companies, many pharmaceutical companies have decided to until recently retain their early stage  drug discovery research  in-house. However, recent advances and developments in automation technology as well as data processing, coupled with the ever-escalating costs of bringing a drug to market, have caused industry to rely more heavily upon academia and other alternative sources for the early stage drug discovery function.

New drug discovery by its nature is an incredibly expensive endeavor and major pharma continues to endure this cost and identify opportunities for cost reduction as they work toward the next big blockbuster. In simple terms, the lines of therapy that are being developed today such as CAR-T therapy tend to generally be much more complicated than drugs of the past. The low-hanging fruit have already been harvested and patients continually demand a higher standard of care and improved treatment regimens. For example, the introduction of biologics instead of small molecule drugs dramatically increased complexities with not only the drug discovery paradigm but also the manufacturing process and storage requirements.  As such, the costs for discovery and development continue to skyrocket and new technological advances such as CRISPR/Cas9 and IPSCs continually increase the complexity of R&D projects.

The main challenge with the introduction of new technologies is that their adoption requires additional resources above and beyond a company’s existing infrastructure and many of these skills are well outside the core competencies of pharmaceutical companies. For example, rapid advances in many disparate fields such as genomics, machine learning and big data analysis now allow for reduced development costs and accelerated discovery but present the business question of implementation. These complex new fields and technologies lead to pharmaceutical companies more commonly evaluating their drug discovery competencies and determining which functions should be internalized and focused on and which ones are best executed by a third party.

Like we saw proliferate in the 1980’s and 90’s, companies are realizing that they can’t afford to have all the toys in the toy box. Back then, as companies expanded product lines from simple solid tablets, to liquids, patches and later pre-filled syringe dosage forms, they realized that maintaining multiple various production lines was inefficient and costly. This lead to the growth of the “contract manufacturing” market segment. A contract manufacturer that specializes in liquid dosage forms, can do it better and faster and most importantly, less expensive than the big pharma company. The capital expenditure and the annual maintenance dollars required to operate a large scale production plant can be better spent in other areas of the company.

The same basic concept applies in the new age of drug discovery that we are now in. For example, Big Pharma does not need to be in-house experts in advanced in vitro models, automation, high-throughput screening, imaging, data processing and machine learning.   There are a host of new discoveries and associated companies that have already done the early heavy lifting and have specializations in these functions. The most successful Big Pharma companies going forward will be the ones that embrace the technology advances in drug discovery and find ways to work with – outsource – the early stage discovery operations to these new players.

Visikol Outsourcing of in vitro assays in drug discovery

3D Tissue Imaging – How Much Data is Too Much Data?

Over the last decade we have gained the ability to transition from traditional two-dimensional slide-based histopathology for tissue characterization to a three-dimensional approach. This transition is possible due to advances in tissue clearing, fluorescent labeling, optical microscopy (e.g. light sheet, confocal, two-photon) and advanced serial sectioning devices (e.g. knife edge scanning microscopy).

However, before we dive into the specifics of 3D tissue imaging, it is important to recognize one very important detail – 3D tissue imaging (i.e. 3D histology) is not beneficial for all applications and will not replace slide-based imaging. 3D tissue imaging is generally only helpful for complex/heterogenous tissues or answering questions that require spatial information (e.g. antibody penetration) where traditional 2D tissue imaging is limited.

The application of 3D tissue imaging allows for whole tissues to be investigated in their entirety and for complex features (e.g. vasculature, neurons) to be quantitatively assessed. With high resolution optics and multi-channel imaging, terabytes of data can now be collected from a single tissue sample while providing a 3D context. Though this seems like a huge advantage for extracting actionable insights from tissues, this volume of data can create significant challenges for researchers for a variety of reasons such as data transfer and processing requirements.

We see this problem commonly and see it as a fundamental misunderstanding with how many researchers perceive 3D tissue imaging. While we can now generate terabytes of data from tissues, the question we should ask ourselves before doing this is:

“What data set do I need to generate to answer my specific research question?”

The misunderstanding here is that more data is always better where the reality is that most of the data we generate from 3D imaging is superfluous. The reason why this is so important for 3D tissue imaging is that we are adding a third-dimension to our imaging and thus when you move from a 10X objective to a 40X objective you will increase your image acquisition time and data density by a factor of 64. From a practical perspective, this means going from a 4-hour imaging session to a 5-day imaging session and if you are using an imaging core at $100 an hour this means $12,800 instead of $400.

For this reason, we always suggest that researchers work to acquire as little data as possible to address their research question. Start with a small region of interest and determine the minimum tissue volume required for your research as well as the minimum imaging parameters (objective, z-step size, pixel size, exposure time). From our experience, the only applications that require extremely large data sets are those which study extremely small features across large volumes or virtual reality applications where high-resolution renderings are required for an optimal user experience.  

 Precision cut lung slice labeled with DAPI, CD68 and lectin - Z-projection.

Precision cut lung slice labeled with DAPI, CD68 and lectin - Z-projection.

Confocal Microscopy for the Three-Dimensional Imaging of Cleared Tissues

Since the introduction of the microscope, tissues have been characterized according to the traditional histological paradigm that relies upon sectioning whole tissues into ultra-thin 2D slices. These slices are then placed on glass microscope slides, labeled and qualitatively interpreted by a researcher or pathologist. While this approach is foundational to all life science fields, it is limited in its ability to characterize complex and heterogeneous tissues. Therefore, since the advent of traditional 2D microscopy, researchers have worked to develop 3D microscopy techniques that characterize tissues in their entirety.

Tissue Clearing

One of the major challenges with the 3D microscopic imaging of tissues is that due to their opacity, optical imaging is limited to a depth of a few cell layers. Therefore, researchers have focused on developing techniques for increasing imaging depth that reduce opacity and these techniques are referred to as tissue clearing techniques. There are currently over a dozen tissue clearing techniques including CLARITY, CUBIC, Scale, SeeDB, Visikol® HISTO™, FocusClear™, i/3/uDISCO and BABB. Each one of these tissue clearing techniques has its own specific advantages and disadvantages and none are suited for all applications.

Fluorescent Labeling

3D microscopy depends equally on tissue transparency as well as fluorescent labeling. Fluorescent proteins (e.g. GFP, RFP, YFP, tdtomato), immunolabels, chemical dyes and various other techniques can be paired with tissue clearing to allow for protein specific imaging. However, a researcher needs to thoroughly understand the compatibility of their tissue clearing technique with labeling approaches as each technique will have different protocol considerations for tissue types and labels. The most common problem that researchers have in imaging tissues in 3D is achieving uniform labeling and it is highly suggested that a researcher starts small and optimizes labeling as they work their way up in tissue thickness.


Once a tissue has been cleared and labeled, the last step before data processing is to image the tissue using either light sheet, 2-photon or confocal microscopy. Confocal microscopy is the most ubiquitous type of 3D imaging technique and is ideal for high resolution/small volume imaging. Larger volumes such as whole rodent brains can be imaged using confocal microscopy, but it will take considerably longer than light sheet microscopy. Prior to imaging, there are a couple of important features of your confocal microscope to take note of. The first of which is whether the confocal microscope is inverted or upright. If the confocal is inverted, then the maximum imaging depth of the system will be limited to approx. 2 mm due to mismatches in refractive index.

If the system is upright, then it is possible the system can be used to conduct whole rodent brain imaging. However, whole brain imaging requires the use of dipping objectives which may or may not be compatible with your tissue clearing technique as many will be destroyed by solvent based techniques (e.g. i/3/uDISCO, Visikol HISTO, BABB). If instead of using dipping objectives you choose to use common air objectives, then your imaging depth will be limited to approx. 2 mm. It is important to note that the working distance of the objective is also very important as oil dipping objectives will be highly limited in imaging depth due not to light attenuation but their working distance.  

Principles of Microscopy - Visikol

Confocal Microscopy Overview

The purpose of confocal microscopy and other 3D microscopy technique are to only capture data from a single z-plane in a tissue at a time. The way confocal microscopes achieve this is through the use of a pinhole mechanism where photons of light outside of the desired z-plane are rejected by a narrow pinhole of approx. 30-70 um. Confocal microscopes illuminate a cleared tissue sample with either a laser or LED at a specific wavelength where the light source passes through all z-planes in the tissue. The resulting fluorescence which is a different wavelength than the excitation wavelength is then passed through the pinhole mechanism by use of a dichroic mirror. By moving the tissue through the focal plane of the objective, numerous optical z-planes can be acquired from a cleared tissue. These z-planes can then be stitched together with other z-stacks to create 3D histological data sets for multiple markers from whole tissues.

Confocal microscope - pinhole - Visikol

Myth: Fluorescent Labels Cannot Penetrate All the Way Through 3D Cell Culture Models

One of the major challenges in using 3D cell culture models is fully utilizing their three-dimensionality and improved mimicry of the in vivo micro environment. While there are one dimensional dissolution based assays for characterizing these models, these assays are limited in the quality of data they can collect and thus researchers are highly interested in image based end points. However, the major problem with using image based endpoints with 3D cell culture models is characterizing all of the cells throughout the depth of these models.

If you were to look at any recent publication on 3D cell culture models that uses image-based end points you will see that optical Z slices look like donuts where the center of the Z slice is black and only the cells in the periphery of the model are characterized. Often, confocal Z stacks are used to illustrate 3D cell culture models, but these depictions miss-represent the data as this inherent bias is obscured. It has long been thought that this bias was due to the inability of labels (e.g. chemical dyes, antibodies) to penetrate into 3D cell culture models and thus it was only possible to characterize peripheral cells.


Widefield Microscopy

 NCI-H2170 spheroids approx 250 um in diameter labeled with SYTOX green nuclear stain. Left is in PBS and right is the same spheroid after clearing with Visikol HISTO-M.

NCI-H2170 spheroids approx 250 um in diameter labeled with SYTOX green nuclear stain. Left is in PBS and right is the same spheroid after clearing with Visikol HISTO-M.


Confocal Microscopy

 NCI-H2170 spheroids approx 250 um in diameter labeled with SYTOX green nuclear stain. Left is in PBS and right is the same spheroid after clearing with Visikol HISTO-M.

NCI-H2170 spheroids approx 250 um in diameter labeled with SYTOX green nuclear stain. Left is in PBS and right is the same spheroid after clearing with Visikol HISTO-M.

At Visikol, we are focused on the clearing, labeling and imaging of tissues and have shown an ability to label and image whole mouse brains with imaging depths as thick as 6 mm. Therefore, when we first heard about the inability to uniformly label 3D cell culture models, we were intrigued as these models typically have a diameter of less than 500 µm. It was our suspicion that this limitation was due not to the inability to uniformly label these models, but instead a result of optical attenuation. 3D cell culture models tend to be dense clusters of cells and light attenuates significantly, limiting effective imaging depth to approx. 40 to 70 µm.

To evaluate the cause of this imaging limitation, we labeled a 250 µm NCI-H2170 spheroid with a SYTOX green nuclear stain and imaged it using both confocal and wide field microscopy. We then took this same spheroid and cleared it with Visikol® HISTO-M™ and re-imaged it using both confocal and wide field microscopy. Through the application of clearing we demonstrated that the amount of cells characterized in the 3D model increased 3-4 fold across both imaging techniques. We also showed that the interior of the model was able to be characterized in its entirety following the application of tissue clearing.

Therefore, we were able to show that the characterization of whole 3D cell culture models is generally not limited by label penetration but instead optical attenuation which can be mitigated with a tissue clearing reagent. However, it must be noted that labeling time and concentration needs to be optimized for each individual label as some will take longer and higher concentrations than others to achieve uniform whole model labeling.

Visikol SLAS 2018 Posters

Ahead of the SLAS conference next week in San Diego we wanted to share the 3D posters that we will be presenting. These posters will be broadly focused on the enhanced imaging of 3D cell culture models (e.g. organoids, microtissues, spheroids) through the use of Visikol HISTO-M tissue clearing.

We will be discussing the use of Corning Ultra Low Attachment Spheroid Well Plates for the easy culturing of 3D cell culture models as well as the features of the GE IN CELL 6000/6500 and the CellInsight CX7


Visikol HISTO Tissue Clearing Protocol Featured on Abcam Website

Visikol is excited to announce that the Visikol HISTO protocol builder tool is now featured on the Abcam website so that all of their customers can now easily extend their histological imaging into a new dimension. Abcam is the leading supplier of protein research tools, and their antibodies pair very well with the Visikol HISTO tissue clearing approach. Visikol CSO and Co-Founder Dr. Thomas Villani remarked, “we are committed to providing researchers with the easiest-to-use solution for tissue clearing and 3D imaging and are excited to share our Visikol HISTO approach with Abcam’s community.”

In the last five years since the introduction of the CLARITY technique by researchers at Stanford University, the field of tissue clearing has rapidly advanced. Today, researchers have at their fingertips the ability to image entire tissues microscopically in 3D where just a few years ago they were limited to a two-dimensional histological context. However, one of the problems in the field of tissue clearing is that because the field has grown so rapidly, there are dozens of tissue clearing techniques such as CLARITY, CUBIC, PARS, PACT, Scale, i/3/uDISCO, FocusClear and Visikol HISTO and it is very challenging for a researcher to determine which protocol is right for their specific research question. Because of this, many researchers attempting tissue clearing have been very disappointed with the results, experiencing poor tissue clearing, some poor labeling, some poor imaging and other totally unexpected and other seemingly unexplained problems. 

The reason for this confusion is twofold: 1) every tissue clearing technique has its own individual advantages and disadvantages, and 2) every different type of tissue (e.g. age, fixation, type, size, species) will require different processing steps for optimal tissue clearing and labeling. Additionally, the adoption of tissue clearing requires that a researcher have experience in complex fields outside of their typical domain, like a proficiency in 3D microscopy, complex data analysis, tissue processing and tissue labeling.

To address these shortcomings and to reduce the adoption hurdles of tissue clearing, Visikol developed their patented Visikol HISTO tissue clearing technique in 2016 and tested it with over 400 research labs from around the world. This customer-centric testing allowed Visikol to intimately understand all of the shortcomings of Visikol HISTO and to develop specialized protocols for each and every application as well as detailed guidance. So along with the launch of the Visikol HISTO product in 2016, Visikol launched its Visikol HISTO protocol builder tool to capture this knowledge, allowing researchers to build customized protocols for every application. The protocol builder tool customizes the Visikol HISTO protocol for tissue type, fixation type, labeling, imaging modality, imaging objective, background fluorescence, tissue thickness and tissue origin to help researchers get the best results possible, the first time they try the technology. Learn more at or

Visikol HISTO on Abcam Website

Visikol HISTO and Abcam

Visikol for Plant Biology as a Replacement for Melzer’s Reagent

Melzer's reagent or Melzer’s solution is a chemical reagent that is used by mycologists to assist with the identification of fungi. The Melzer’s reagent consists of chloral hydrate potassium iodide, and iodine. While Melzer’s reagent is an effective tool in the mycologists tool box, it is difficult to obtain due to chloral hydrate being a regulated narcotic in many countries including the US. To ameliorate problems with obtaining chloral hydrate, Visikol has developed Visikol® for Plant Biology™ which can be used in a modified protocol to replace Melzer’s Reagent. Visikol® for Plant Biology™ was originally developed as a tissue clearing reagent for plant tissues as a replacement to chloral hydrate and can be adapted as a replacement for chloral hydrate in Melzer’s reagent.

Typically, Melzer’s reagent works by exposing fungal tissue or cells to the reagent on a microscope slide and looking for the color of the reaction:

1)    Blue to Black – Amyloid or Melzer’s positive

2)    Brown to Reddish Brown - Pseudoamyloid or dextrinoid reaction

3)    No change or yellow-brown – Amyloid or Melzer’s negative

Amyloid positive fungi can be furthered identified based upon their color change with the addition of a potassium hydroxide pretreatment step.

1)    Blue without pretreatment - Euamyloid reaction

2)    Red in Lugols solution/ Blue in Lugols or Melzer’s with pretreatment - Hemiamyloid reaction

Replacement for Melzer’s Reagent Protocol

1.    Crush sample in ethanol using mortar and pestle so that it is fine enough for a microscope slide. If the sample is already a scraping or fine enough for microscope visualization only wash in ethanol.

2.    Let sample air dry or gently heat.

3.    Place drop of iodine solution (lugols) on sample and then let it air dry or gently heat.

4.    Place Visikol® for Plant Biology™ on sample until clear and let dry.

5.    Mount sample in 100% glycerol (non-permanent) or permanent mounting solution (e.g. Visikol® Mount™)

6.    Visualize sample on microscope.

**If conducting pretreatment with KOH, solution pH must be neutralized before proceeding with protocol**

Buy Visikol for Plant Biology

Visikol for Plant Biology