Biological imaging data has massive potential in terms of complexity of experiment and richness of dataset. While early imaging experiments were mostly descriptive, showing if "target X presents in cell of interest;" modern experiments have the potential to massively multiplex, define complex spatial relationships, measure levels or numbers of tiny subcellular components, and even measure mRNA.
There are a number of approaches being taken by researchers to generate larger, more complex data sets. Some groups are trying to advance the understanding of systems biology by looking at tens (or hundreds!) of proteins with techniques like CyCIF1 or MIBI mass cytometry2 and high-plex commercial offerings from Fluidigm (CyTOF), Nanostring (GeoMx), and Neogenomics (MultiOmyx). Others are taking a machine learning approach to quantify and define cell populations and spatial relationships with techniques like histocytometry3, image cytometry4, and digital pathology5.
Yet others are looking at protein staining at the organ level with tissue clearing techniques like CLARITY6. Regardless of the platform, all of these techniques enable more image data than ever before. With so many options available, when should you consider performing a tissue clearing experiment? This technique is most appropriate when you want to study a fine detail at the macroscopic level.
A beautiful video of a cleared mouse brain recently produced by the lab of Kwanghun Chung, inventor of CLARITY, shows GFAP positive astrocytes and NeuN positive mature neurons and their positional relationships throughout the brain. This technique not only produces visually stunning renderings, it is also a useful tool for neuroscientists to study fine details in a large-scale analysis. The brain is an especially complex structure, where knowing both whole-organ and local details can provide useful information to a researcher.
While the number of neurons in the brain is relatively static, astrocytes are a continually changing population of cells. Looking at the relationship between neurons and astrocytes is extremely informative in the context of disease7. GFAP is a structural protein unique to astrocytes8. In addition to the role of astrocytes in the healthy central nervous system providing structural and functional support to neurons, reactive astrocytes respond to damage and disease in the central nervous system through the process of astrogliosis9.
NeuN is now known to identify RBFOX3 but was originally found in a histological screen for neuron-specific antibodies10. The function of NeuN is not entirely clear, but this marker is used because it stains the nuclei of most neurons. Aside from the beautiful data featured in this video, one could imagine looking at GFAP and NeuN using CLARITY to study models of brain injury from head trauma, transient ischemia and strokes, neurodegenerative diseases, and cancer.
Looking at data on the whole organ level opens a new level of analysis that has been long neglected in biological imaging – spatial relationships. Positional information provides useful data for tumor biology, organ, and systems biology (within the context of size, position, and organization), and the developing embryo. These are all areas that may benefit from large-scale analysis of cleared tissue. With that being said there are several challenges associated with CLARITY and other tissue clearing techniques.
First, specialized objectives are required for long-distance imaging11. Also, experiments take a long time due to the speed at which tissue is cleared and antibody is dispersed throughout the sample (several days for each step). Furthermore, there is a high cost to perform an experiment due to the amounts of reagents required. Moreover, not all antibodies that work for immunofluorescence will work for tissue clearing techniques12. Finally, working with, storing, and displaying massive image files is also challenging. Regardless, CLARITY is a remarkable technique and I think the result is well worth the effort!
Learn more about the Clarity imaging technique.
- Gerner M, Kastenmuller W, Ifrim I, Kabat J, Germain RN. Histo-Cytometry: A Method for Highly Multiplex Quantitative Tissue Imaging Analysis Applied to Dendritic Cell Subset Microanatomy in Lymph Nodes. Immunity 2012; 37(2):364-376.
- Tsujikawa T, Thibault G, Azimi V, Sivagnanam S, Banik G, Means C, Kawashima R, Clayburgh DR, Gray JW, Coussens LM, Chang YH. Robust Cell Detection and Segmentation for Image Cytometry Reveal Th17 Cell Heterogeneity. Cytometry A. 2019 Apr; 95(4):389-398.
- Komura D, Ishikawa S. Machine Learning Methods for Histopathological Image Analysis. Comput Struct Biotechnol J. 2018;16:34–42.
- Phatnani H, Maniatis T. Astrocytes in Neurodegenerative Disease. Cold Spring Harb Perspect Biol. 2015 (6): a020628.
- Yang Z, Wang KK. Glial fibrillary acidic protein: from intermediate filament assembly and gliosis to neurobiomarker. Trends Neurosci. 2015; 38(6):364–374.
- Sofroniew, M. Astrogliosis. Cold Spring Harb Perspect Biol. 2015 (2): a020420.
- Duan W, Zhang YP, Hou X, Huang C, Zhu H, Zhang QC, Yin Q. Novel insights into NeuN: from neuronal marker to splicing regulator. Mol Neurobiol, 2016, 53:1637-1647
- Li W, Germain RN, Gerner MY. Multiplex, quantitative cellular analysis in large tissue volumes with clearing-enhanced 3D microscopy (Ce3D). PNAS 2017;114(35):E7321-E7330