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The cell is the basic unit of our body – and key to understanding the biology of good health, as well as how molecular dysfunction leads to disease. Yet our understanding of the hundreds of cell types and subtypes in the human body is still very limited and often based on techniques with insufficient resolution. Classification methods have mostly been applied collectively to assemblies of many cell types, obscuring critical differences between cells.
To address this fundamental knowledge gap, the Human Cell Atlas Project (https://www.humancellatlas.org/), which resembles the Human Genome Project, was launched a few years ago. An international community of biologists, clinicians, technologists, physicists, computer scientists, software engineers and mathematicians has teamed up to work on this project. This community of scientists with diverse areas of expertise shares the common goal of creating a comprehensive reference map of all human cells as a basis for understanding human health as well as diagnosing, monitoring and treating diseases [1]. Without such a map of the different cell types, their location in the body and the genes they express it is impossible to understand the many cellular activities and describe their underlying biological networks [2]. New tools such as single-cell genomics and omics technologies have now brought this goal within reach for the first time.
Fig. 1 A) Partially dissociated liver tissue; B) Hepatocytes from liver tissue dissociated using the Tis-sueGrinder.
The vast majority of these technologies require high-quality single cells for analysis, i.e. cells that have been detached from a tissue as gently as possible (Fig. 1). Such cells need to be taken from tissue samples efficiently (Fig. 2). But cell viability, cell yield and the effort needed to perform the task are not all that matters. It is particularly important that cell diversity and cell function are preserved.
There are many preanalytical variables that can affect the accuracy and reliability of single-cell analyses. However, standardizing these variables is difficult due to the variety of manual methods used to collect and process samples. Many steps along the processing chain from the tissue to single cells, including sample acquisition, sample transportation and post-dissociation processing (e.g. nucleic acid extraction, library preparation, sequencing method choice), are critical for the reliability of single-cell analyses [3].
Unsurprisingly, a system as complex as the human organism contains tissue types that span a remarkable spectrum of stiffness. The elastic modulus ranges from 11 Pa for intestinal mucus to 70 kPa for pre-calcified bone cells, roughly equivalent to the stiffness of gumdrops [4]. Between these extremes, almost all orders of magnitude are represented by a corresponding tissue. Neural tissues are among the softest in the human body, which is unsurprising given their anatomical protection and how easily they can be damaged. Most abdominal organs (such as pancreas, spleen and liver) are somewhat less soft, followed by muscles and supporting structures such as cartilage, tendons, ligaments and, at the end of the spectrum, the remarkably stiff bones [5].
Fig. 3 Cells dissociated with the TissueGrinder from the cortex and hippocampus of adult mice (gre neurons, red: astrocytes, blue: nucleus).
The diverse characteristics of cell samples make it difficult to standardize and automate tissue processing. Dissecting an organ or tissue into a single-cell suspension to allow single-cell analysis can be a lengthy and challenging process. Different tissues require different dissociation protocols, so there is no universal method. Embryonic tissues tend to be easy to dissociate, while adult tissues (Fig. 3) are usually more challenging. Diseased and fibrotic tissues can be particularly difficult to dissociate [6]. Tissue dissociation is mostly still performed manually using a scalpel, enzyme digestion and cell strainers. Early automated approaches were also based on prior treatment with enzymes. However, this can alter the morphology and surface markers of the cells, which can drastically reduce their diagnostic potential. The enzyme-free and fast TissueGrinder technology, developed by Fraunhofer, overcomes this fundamental bottleneck of the processing chain and can unleash the diagnostic potential of new technologies (Fig. 4).
Fig. 4 TissueGrinder Fraunhofer IPA. The TissueGrinder technology enables standardized sample prepa-ration and subsequent filtration in a closed, sterile system using standard laboratory equipment.
This technology for purely mechanical dissociation of tissue samples, now licensed out to Fast Forward Discoveries GmbH, is based on the adapted geometry of a grinder in which cutting and shearing forces alternate. It produces largely unadulterated, high-quality single cells from tissue samples within a few minutes, and without the need for enzymes. This dissociation technique can be integrated into common laboratory formats and thus into existing processes. It can also be combined with an optional prior enzymatic treatment.
The range of possible applications and analytical techniques is diverse. A prominent example is the analysis of lymph nodes to ensure that a tumor has not already spread. At present, histological sections are still visually inspected by a pathologist, in the course of which only part of the lymph node can be examined. This is why tumor cells between the sectional planes remain undetected. Dissociation of the entire lymph node into single cells allows tumor involvement to be accurately quantified. This can be followed by genetic characterization of the tumor cells [7], as detecting specific mutations is critical for selecting the state-of-the-art drugs for a personalized therapy [8]. In addition, a higher throughput of sample material can be achieved by simultaneous tissue processing.
The next steps in single-cell technology could include analysis and sorting based on size, structure or further physical properties such as elasticity [9] or specific fluorescence-labeled surface structures, as well as genetic sequencing or analysis using mass spectrometry. There is also an enormous and as yet largely untapped potential for tissue samples that pathology departments fix and preserve in formalin and paraffin (FFPE). The advantage of FFPE tissue is that its morphology remains well preserved. The disadvantage is fixation – cross-linking and thus blockage of antigen epitopes occurs over a longer period of time. To make this invaluable and accessible resource of biological samples available for high-resolution genome analyses, Fraunhofer IPA is working with clinical cooperation partners (Prof. Gaiser & Dr. Hirsch, University Hospital Mannheim) on a workflow to generate single cells from FFPE samples.
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Kategorie: Single Cells | Tissue Dissociation
Literature:
[1] Regev, A., Teichmann, S.A., Lander, E.S., Amit, I. et al. (2017) The Human Cell Atlas, Elife, 2017 Dec 5;6:e27041, DOI: 10.7554/eLife.27041
[2] Tong, H., Gao, S., Long, X., Xie, L. (2021) A Neural Lyapunov Approach to Transient Stability Assessment in Interconnected Microgrids, http://hdl.handle.net/10125/71020, 2021 Jan 05, DOI: 10.24251/HICSS.2021.405
[3] Ascierto, P.A., Ferrucci, P.F., Fisher, R., Del Vecchio, M. et al. (2019) Dabrafenib, trametinib and pembrolizumab or placebo in BRAF-mutant melanoma, Nat. Med., 25 (6), 941-946, DOI: 10.1038/s41591-019-0448-9
[4] Irianto, J., Pfeifer, C.R., Bennett, R.R., Xia, Y. et al. (2016) Nuclear constriction segregates mobile nuclear proteins away from chromatin, Mol Biol Cell, 27 (25), 4011-4020, Epub 2016 Oct 26, DOI: 10.1091/mbc.E16-06-0428
[5] Guimarães, C.F., Gasperini, L., Marques, A.P., Reis, R.L. (2020) The stiffness of living tissues and its implications for tissue engineering, Nat. Rev. Mater., 5, 351-370, DOI: 10.1038/s41578-019-0169-1
[6] Potter A.S., Steven Potter S. (2019) Dissociation of Tissues for Single-Cell Analysis; in: Vainio S. (eds) Kidney Organogenesis. Methods in Molecular Biology, vol 1926, Humana Press, New York, NY, DOI: 10.1007/978-1-4939-9021-4_5
[7] Ulmer, A., Dietz, K., Hodak, I., Polzer, B. et al. (2014) Quantitative Measurement of Melanoma Spread in Sentinel Lymph Nodes and Survival. PLoS Med 11 (2): e1001604, DOI: 10.1371/journal.pmed.1001604
[8] Dietel, M., Jöhrens, K., Laffert, M. et al. (2015) A 2015 update on predictive molecular pathology and its role in targeted cancer therapy: a review focussing on clinical relevance, Cancer Gene Ther, 22, 417–430, DOI: 10.1038/cgt.2015.39
[9] Otto, O., Rosendahl, P., Mietke, A., Golfier, S. et al. (2015) Real-time deformability cytometry: on-the-fly cell mechanical phenotyping, Nat. Methods 12 (3), 199-202, 4 p following 202, DOI: 10.1038/nmeth.3281
Stefan Scheuermann, born in 1989, studied biotechnology at Mannheim University of Applied Sciences, graduating with a Master of Science (M.Sc.) Biotechnology-Bioprocess Development. After a subsequent research stay at the National Centre for Sensor Research (NCSR) in Dublin, he began his ca ... more
Jens Langejürgen, born in 1981, studied physics at RWTH Aachen University, then worked in the field of biomedical optics on methods of non-invasive temperature measurement at the eye fundus. He then completed his doctorate at the Leibniz University Hannover at the Institute of Electrical En ... more
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