At Visikol, we have a team of experts in immunohistochemistry and tissue imaging. We employ confocal microscopy to achieve the best possible resolution of tissue structures. Utilizing confocal microscopy in conjunction with tissue clearing allows for three-dimensional image stacks of tissues to be obtained.
We provide tissue imaging services for sections and 3D imaging of whole mount preparations or thicker tissue specimens. Furthermore, we offer a suite of analytical services to provide comprehensive image processing, including, but not limited to, cell counting, colocalization analysis, and machine learning-based classification of tissue image sets.
Clients work with us in a number of different ways, employing our services to conduct any or all of the following:
Obtaining and preparation of tissue specimens
Antigen retrieval and immunolabeling
Tissue clearing (for thicker specimens)
Image processing to extract quantitative and semi-quantitative data
Data analysis, statistical analysis and/or informatics
We commonly employ fluorescent probes and/or immunolabeling for labeling targets of interest in tissue samples. We offer single and multiplexed immunolabeling for tissues. We have developed a suite of protocols for deep-tissue immunolabeling and routinely achieve 1-2 mm tissue depth with most labels. For projects requiring labels we have not yet employed, we begin with a label optimization step. We only use antibodies validated for immunohistochemistry, and we optimize dilutions and processing to ensure uniform labeling and penetration of the antibodies.
At Visikol, we specialize in imaging whole mount preparations, thick tissue sections, and biopsies/necropsies. We are experts in 3D tissue imaging and have developed protocols and guidance for how best to image a wide range of different tissue specimens, ranging in size from small tissue biopsies up to whole mouse brains.
For thick tissue specimens (> 3 mm), we find that cutting tissues into 1-2 mm thick sections increases uniformity of labeling and drastically reduces tissue processing and imaging time. Once we have completed labeling, we clear tissues with our patented Visikol® HISTO™ tissue clearing technique so that they can be imaged in 3D. This technique is preserves tissue morphology and is fully reversible, allowing us to execute follow-up section-based H&E and IHC if needed for higher resolution studies.
This approach greatly improves throughput and simplifies data analysis. Tissue thickness is the most important consideration in 3D tissue imaging as it will dictate the type of microscope and objectives that can be used as well as how long labeling will take. Whole, intact mouse brains can take weeks of processing compared to 48 hours for 1 mm mouse brain section.
Our practical approach to 3D tissue imaging means that you will get results as fast as possible at the lowest possible price. For specialized studies that require analysis of much thicker tissues and intact organs, we can employ light sheet microscopy and dipping objectives for deep tissue imaging but find these techniques to only be useful only in unique circumstances.
Once a tissue has been imaged we utilize our image analysis platform, known as 3Screen™. Using our custom-built platform, images are analyzed to extract quantitative features such as cell counts, breakdown of percentage of cells expressing specific labels, organizational changes to the cytoskeleton, ratios of various biomarkers, total area of vessels and number of vessel branches, orientation distribution tissue structures, etc. We can quantify any aspect that can be visually identified in an image, and frequently work with clients to define customized metrics to extract and analyze from tissue image sets.
After quantitative data is obtained from tissue datasets, we use our analytical pipelines to evaluate trends occurring within the various groups of data. We use statistical techniques to examine the significance of differences within and between tissues, and we can employ informatics approaches to uncover trends and evaluate large sets of data. We can also apply supervised or unsupervised machine learning approaches to classify the data extracted from tissue images, to further examine relationships and trends within an experiment.