Whole organ preservation, active clearing and labeling, and light sheet imaging have solved many issues related to thin-section histology, including high consumption of time and resources, spotlight bias, and realignment errors. LifeCanvas’ tissue processing pipeline is the world’s only end-to-end solution for processing whole organs from preservation to analysis.

LifeCanvas Portraits: Dr. Jose Maldonado

Jose Maldonado, Managing Director of Vanderbilt’s NeuroVisualization Lab, discusses his neuroscience research interests and how LifeCanvas’ imaging and analysis platforms helped quantify crucial data in his co-authored study published in Nature Communications this June.

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Three easy steps to superior tissue preservation with SHIELD

In this blog, we will provide key insights in the first step of this journey: tissue preservation using SHIELD. SHIELD is a tissue-gel hybridization method using polyfunctional, flexible epoxides to preserve tissue architecture, endogenous fluorescence, protein antigenicity, and nucleic acids.

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Cleared lung tissue allowing visualization of the collagen structure and matrix of lung tissue at 20x magnification. Bharat et al., 2021.

How 3D tissue clearing benefits COVID-19 research

Over 190 million people have now been infected with the novel coronavirus SARS-CoV-2 in the ongoing coronavirus disease (COVID-19) pandemic with a total number of deaths passing 4.1 million worldwide. Multiple organs are affected by COVID-19, however, the lungs are the main site of disease.

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Mouse brain viral expression

Recent publications leverage 3D histology to provide new insights into neuronal circuitry

The goal of histology has long been to interrogate complex structures in whole tissue samples. Over centuries, slicing of samples for traditional two-dimensional histology has been indispensable in understanding the structure and morphology of multiple tissue types. However, this approach is limited to yielding images in thin tissue sections, is time-consuming and is error-prone due to tissue distortion and information loss.

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Accuracy of training data by number of users making annotations, for cell detection algorithm

How to create accurate training data for cell classification algorithms

Creating high-quality training data is a critical step when designing algorithms to detect and classify cells. Training data are example data points that are used to develop an algorithm. For example: a set of images, each of which might or might not contain a cell, plus human-generated tags indicating whether each image actually contains a cell.

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Mouse spinal cord expressing tdTomato, imaged with SmartSPIM

Upcoming publications to watch

Check out these upcoming publications utilizing LifeCanvas products and technologies! Our tissue processing methods and devices are utilized around the world to further medical and biological research, facilitating new discoveries and improving lives. The articles below are posted prior to peer-review in open-access medRxiv and bioRxiv online repositories.

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