Commentary: Live Imaging Followed by Single Cell Tracking to Monitor the Cell Biology and Lineage Progression of Multiple Neural Populations

Felipe Ortega1,2,3*

1Biochemistry and Molecular Biology Department, Faculty of Veterinary medicine, Complutense University, Madrid, Spain

2Institute of Neurochemistry (IUIN), Madrid, Spain

3Health Research Institute of the Hospital Clínico San Carlos (IdISSC), Spain


Live imaging and single cell tracking enables researchers to monitor crucial aspects of the biology of neural populations. In this commentary, we highlight the requirements, applications, and limitations of a protocol recently published by our research group. This protocol involves adapting the culture of several types of neural cells to time-lapse video microscopy, and the post-processing of the data to track distinct cell populations.


In order to extract reliable information from lineage tracing experiments, it is essential that they are tightly controlled. In a perfect lineage tracing experiment, the cell that serves as the founder is specifically labelled, thereafter transmitting this label to all the progeny without it spreading to unrelated cells and without modifying the physiological behavior of the cells1. This is particularly important when dealing with dynamic cells with a conspicuous regenerative potential, such as neural stem cells (NSC)2-4. Although several approaches have commonly been used to identify cell populations and their progeny, they may easily lead to drawing inappropriate conclusions given the inherent heterogeneity in cell behavior or the properties of the isolated cells in culture1, 5. Moreover, most of the current methods used to identify the mechanisms controlling the cell biology and lineage progression of NSCs rely on static end-point analyses, that are often hampered by the consequences of dilution or cell loss, possibly leading to misinterpretation of the results5, 6. In fact, slight variations in crucial features, such as cell cycle length, migration, cell fate decisions or viability might pass unnoticed when employing this type of analysis. This lack of information is especially problematic when we aim to design effective therapeutic strategies for brain repair. Despite constituting one of the oldest scientific approaches7, live imaging and single cell tracking provides real-time information over the length of an experiment, thereby allowing variations in the events listed above to be described precisely5, 8-12. The efficacy of live imaging experiments is not only restricted to the study of NSCs but it can also be applied to the study of differentiated neural populations that retain a strong regenerative potential in specific pathological circumstances, such as astrocytes in traumatic brain injury13 or oligodendrocytes in demyelinating diseases14-16. Likewise, monitoring a single cell is essential to detect the morphological and molecular changes associated to the conversion of one cell type into another through direct reprogramming, currently one of the most promising therapeutic strategies for neural diseases17-21.

We recently presented a feasible protocol that enables researchers to perform single cell tracking by time-lapse video-microscopy, and which can be followed by data post-processing22. This protocol can be easily adapted to multiple neural populations by following some specific premises described in the manuscript, thereby addressing fundamental questions regarding the cell biology or lineage progression of these cells. There are some important requirements and considerations that must be taken into consideration in order to successfully design and perform a live imaging experiment. First, time-lapse video-microscopy needs adequate hardware, including microscopy systems, most frequently brightfield and phase contrast microscopes, and usually in combination with motorized components supplying a level of automation that is vital for long-term experiments (i.e.: stages, shutters, and filters). In addition, incubation systems and permeable plates are necessary to maintain the correct CO2 saturation and pH during the experiment, which is indispensable to ensure cell viability throughout. Moreover, the detection of molecular markers commonly requires epifluorescence devices. Epifluorescence is needed to detect specific cell labelling produced by the use of transgenic animals or exogenous cell labelling achieved by transfection or transduction. Furthermore, identifying the nature of the progeny often involves post-imaging immunocytochemistry, which may also rely on epifluorescence. Besides the microscopes and their accessories, time-lapse video microscopy also requires robust data storage systems and reliable software to process the enormous -amount of data produced during long term live imaging experiments. An average experiment over 120 hours in which brightfield images are acquired every 5 min will require around 120-150 gigabytes of storage on a computer linked to the microscope. In our manuscript, we use the NIS-Elements software from Nikon to control the number of imaging fields, the frequency of acquisition and the duration of the experiment, although these features can be readily be controlled by any imaging software that contains a time-lapse module. It is also important that when setting the experimental conditions, over-exposure to transmitted, or particularly fluorescent light, should be avoided as this may compromise cell viability due to its inherent phototoxicity. Hence, it is wise to define an adequate interval to establish a balance between the temporal resolution of the analysis and the potential cell death. We also describe the use of dedicated software for single cell tracking that was developed by the group of Dr Timm Schroeder, namely The Tracking Tool (tTt)23. This software is freely available, and it can be downloaded, together with the instructions for its installation and use, at: https://www.bsse.ethz.ch/csd/software/tTt-and-qtfy.html.

The correct hardware and software settings constitute only part of a successful live imaging experiment. There are also crucial issues regarding the cell populations under study that must be adequately adjusted in order to obtain data of sufficient quality. Probably the most critical issue is the density of the cells in culture. If the cell density is too high or the efficiency of the dissociation is poor, the clumps present in the culture may hinder single cell tracking. Conversely, a low density may compromise the viability of the cells and thus, researchers must standardize their cultures to ensure an optimal cell density prior to commencing the live-imaging experiments. In this line, the researchers need to bear in mind that variations within independent cultures might be high, making indispensable the analysis of enough number of movies and proper sample sizes to guaranty solid readouts. Conclusions based in single or few selected image sequences may lead to misinterpretations. In our manuscript, we define suitable culture conditions and cell densities to successfully image adult NSCs8, postnatal cortical astrocytes24, mouse neuroblastoma cells and cerebellar astrocytes25. In addition, frequent adjustment of the focal distance is essential to obtain high-quality images, a necessary condition for flawless single cell tracking. Likewise, maintaining stable conditions of humidity, temperature and CO2 ensures cell viability throughout the experiment.

Although live imaging experiments provide the researcher with a remarkable amount of data regarding the cell biology of selected cell populations, this approach also has important limitations that must be borne in mind. First, the low-density conditions necessary to ensure reliable single cell tracking makes the use of biochemical assays unfeasible. Moreover, such low-density cultures often compromise the viability of isolated cells over longer periods, limiting the duration of the experiments. Conversely, while rapidly dividing populations often do not suffer the problems of viability, they may rapidly become confluent, presenting a similar restriction to the duration of the live imaging experiment. Second, it is important to consider that cell isolation creates an environment that is not necessarily representative of their physiological niche. While this may have some negative effects, given the loss of signals that may be important for physiological cell behavior, it also represents an opportunity to study the specific role of each extrinsic factor that defines the neural niche, as well as environmental factors that may reproduce pathological conditions, representing one of the true benefits of live imaging experiments9, 10, 17, 26-29.

In summary, the protocol described in our manuscript constitutes a useful resource for researchers in the field of neuroscience. However, given the limitations described here, it is important to dedicate some effort to the future development of methods aimed at following the behavior of single cells in vivo with minimal interference to the physiological environment11.

  1. Kretzschmar K, Watt FM. Lineage tracing. Cell. 2012; 148: 33-45.
  2. Berninger B, Jessberger S. Engineering of Adult Neurogenesis and Gliogenesis. Cold Spring Harb Perspect Biol. 2016; 8.
  3. Alvarez-Buylla A Lim DA. For the long run: maintaining germinal niches in the adult brain. Neuron. 2004; 41: 683-686.
  4. Gage FH, Temple S. Neural stem cells: generating and regenerating the brain. Neuron. 2013; 80: 588-601.
  5. Schroeder T. Long-term single-cell imaging of mammalian stem cells. Nat Methods. 2011; 8: S30-35.
  6. Ortega F, Costa MR. Live Imaging of Adult Neural Stem Cells in Rodents. Front Neurosci. 2016; 10: 78.
  7. Conklin EG. The Mutation Theory From the Standpoint of Cytology. Science. 1905; 21: 525-529.
  8. Ortega F, Costa MR, Simon-Ebert T, et al. Using an adherent cell culture of the mouse subependymal zone to study the behavior of adult neural stem cells on a single-cell level. Nat Protoc. 2011; 6: 1847-1859.
  9. Ortega F, Gascón S, Masserdotti G, et al. Oligodendrogliogenic and neurogenic adult subependymal zone neural stem cells constitute distinct lineages and exhibit differential responsiveness to Wnt signalling. Nat Cell Biol. 2013; 15: 602-613.
  10. Costa MR, Ortega F, Brill MS, et al. Continuous live imaging of adult neural stem cell division and lineage progression in vitro. Development. 2011; 138: 1057-1068.
  11. Skylaki S, Hilsenbeck O, Schroeder T. Challenges in long-term imaging and quantification of single-cell dynamics. Nat Biotechnol. 2016; 34: 1137-1144.
  12. Schroeder T. Imaging stem-cell-driven regeneration in mammals. Nature. 2008; 453: 345-351.
  13. Jassam YN, Izzy S, Whalen M, et al. Neuroimmunology of Traumatic Brain Injury: Time for a Paradigm Shift. Neuron. 2017; 95: 1246-1265.
  14. Abu-Rub M, Miller RH. Emerging Cellular and Molecular Strategies for Enhancing Central Nervous System (CNS) Remyelination. Brain Sci. 2018; 8.
  15. Franklin RJM, Ffrench-Constant C. Regenerating CNS myelin - from mechanisms to experimental medicines. Nat Rev Neurosci. 2017; 18: 753-769.
  16. Clemente D, Ortega MC, Melero-Jerez C, et al. The effect of glia-glia interactions on oligodendrocyte precursor cell biology during development and in demyelinating diseases. Front Cell Neurosci. 2013; 7: 268.
  17. Gascón S, Murenu E, Masserdotti G, et al. Identification and Successful Negotiation of a Metabolic Checkpoint in Direct Neuronal Reprogramming. Cell Stem Cell. 2016; 18, 396-409.
  18. Heinrich C, Spagnoli FM, Berninger B. In vivo reprogramming for tissue repair. Nat Cell Biol. 2015; 17: 204-211.
  19. Peron S, Berninger B. Reawakening the sleeping beauty in the adult brain: neurogenesis from parenchymal glia. Curr Opin Genet Dev. 2015; 34: 46-53.
  20. Masserdotti G, Gascon S, Gotz M. Direct neuronal reprogramming: learning from and for development. Development. 2016; 143: 2494-2510.
  21. Gascon S, Masserdotti G, Russo GL, et al. Direct Neuronal Reprogramming: Achievements, Hurdles, and New Roads to Success. Cell Stem Cell. 2017; 21: 18-34.
  22. Gómez-Villafuertes R, Paniagua-Herranz L, Gascon S, et al. Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations. J Vis Exp. 2017.
  23. Hilsenbeck O, Schwarzfischer M, Skylaki S, et al. Software tools for single-cell tracking and quantification of cellular and molecular properties. Nat Biotechnol. 2016; 34: 703-706.
  24. Heinrich C, Gascón S, Masserdotti G, et al. Generation of subtype-specific neurons from postnatal astroglia of the mouse cerebral cortex. Nat Protoc. 2011; 6: 214-228.
  25. Jiménez AI, Castro E, Mirabet M, et al. Potentiation of ATP calcium responses by A2B receptor stimulation and other signals coupled to Gs proteins in type-1 cerebellar astrocytes. Glia. 1999; 26: 119-128.
  26. Karow M, Sánchez R, Schichor C, et al. Reprogramming of pericyte-derived cells of the adult human brain into induced neuronal cells. Cell Stem Cell. 2012; 11, 471-476.
  27. Kleiderman S, Gutbier S, Ugur Tufekci K, et al. Conversion of Nonproliferating Astrocytes into Neurogenic Neural Stem Cells: Control by FGF2 and Interferon-gamma. Stem Cells. 2016; 34: 2861-2874.
  28. Bunk EC, Ertaylan G, Ortega F, et al. Prox1 Is Required for Oligodendrocyte Cell Identity in Adult Neural Stem Cells of the Subventricular Zone. Stem Cells. 2016; 34: 2115-2129.
  29. Aravantinou-Fatorou K, Ortega F, Chroni-Tzartou D, et al. CEND1 and NEUROGENIN2 Reprogram Mouse Astrocytes and Embryonic Fibroblasts to Induced Neural Precursors and Differentiated Neurons. Stem Cell Reports. 2015; 5: 405-418.
 

Article Info

  • Journal of Neurology & Neuromedicine
  • Article Type : Commentary
  • View/Download pdf
  • DOI : 10.29245/2572.942X/2018/4.1201

Article Notes

  • Published on: August 13, 2018

Keywords

  • Neural stem cells

  • Epifluorescence

*Correspondence:

Dr. Felipe Ortega
Biochemistry and Molecular Biology Department, Faculty of Veterinary medicine, Complutense University
Institute of Neurochemistry (IUIN); Health Research Institute of the Hospital Clínico San Carlos (IdISSC); Madrid, Spain
Telephone No: 0034 91 394 3892
Email: fortegao@ucm.es.
Copyright: ©2018 Ortega F. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.
Citation: Ortega F. Commentary: Live Imaging Followed by Single Cell Tracking to Monitor the Cell Biology and Lineage Progression of Multiple Neural Populations. J Neurol Neuromed (2018) 3(4):85-87