This protocol presents steps to acquire and analyze fluorescent calcium images from brain ensheathing pericytes and blood flow data from nearby blood vessels in anesthetized mice. These techniques are useful for studies of mural cell physiology and can be adapted to investigate calcium transients in any cell type.
Recent advances in protein biology and mouse genetics have made it possible to measure intracellular calcium fluctuations of brain cells in vivo and to correlate this with local hemodynamics. This protocol uses transgenic mice that have been prepared with a chronic cranial window and express the genetically encoded calcium indicator, RCaMP1.07, under the α-smooth muscle actin promoter to specifically label mural cells, such as vascular smooth muscle cells and ensheathing pericytes. Steps are outlined on how to prepare a tail vein catheter for intravenous injection of fluorescent dyes to trace blood flow, as well as how to measure brain pericyte calcium and local blood vessel hemodynamics (diameter, red blood cell velocity, etc.) by two photon microscopy in vivo through the cranial window in ketamine/xylazine anesthetized mice. Finally, details are provided for the analysis of calcium fluctuations and blood flow movies via the image processing algorithms developed by Barrett et al. 2018, with an emphasis on how these processes can be adapted to other cellular imaging data.
The central nervous system vasculature consists of penetrating arterioles, capillaries, and ascending venules. Within this network, mural cells such as vascular smooth muscle cells encase arterioles and pericytes extend cellular processes along the first arteriole branches and capillaries1. Pericytes appear to have several roles within the brain including maintenance of the blood-brain-barrier1,2, migration and motility3, potential stem-cell properties, and the regulation of brain blood flow4,5,6. Many of the functional roles of pericytes have been linked to fluctuations in intracellular calcium that may regulate the dilation or contraction of these cells4,5,6.
Several recent studies have set criteria for identifying different types of brain pericytes7,8. Mural cells within the first 4 branches of penetrating arterioles are ensheathing pericytes based on their expression of the contractile protein α-smooth muscle actin (αSMA) and their protruding, ovoid somata with processes that wrap around vessels7,8,9. To visualize calcium fluctuations in ensheathing pericytes, this protocol uses a novel transgenic mouse line, Acta2-RCaMP1.07, also known as Tg(RP23-370F21-RCaMP1.07)B3-3Mik/J10. These mice express the red genetically encoded calcium indicator, RCaMP1.07, in αSMA expressing cells (vascular smooth muscle cells and ensheathing pericytes). Breeding colonies are maintained by crossing noncarrier animals with hemizygotes. RCaMP1.07 is a red fluorescent protein with a calmodulin binding domain, which increases fluorescence when binding to the intracellular calcium10,11. This protocol outlines the steps for combined calcium imaging of ensheathing pericytes and blood flow measurements by two photon microscopy including procedures for tail vein injection of fluorescent dyes, microscope image acquisition in anesthetized mice, and data analysis with programming platforms (Figure 1). These techniques are useful for addressing questions about mural cell physiology but can be adapted to study calcium transients in any cell type in the brain or other organ system.
A 10-month-old female Acta2-RCaMP1.07 mouse was used for the experiment presented in this article. The mouse underwent surgery for chronic cranial window and head post implantation two months prior. Details for the surgical protocol are discussed in previous studies12,13 and similar procedures have been performed in other previously published protocols14,15. The vasculature is labeled with green fluorescein-Dextran (70,000 MW, anionic solution, 2.5% w/v) injected intravenously. This dye is cost effective and readily available from commercial sources, but it has a wider emission spectrum that may overlap with RCaMP emission and bleed through during microscope image acquisition. Steps for spectral unmixing are outlined in Section 4 below to circumvent this, but other green dyes with narrower emission spectra, such as those based on EGFP, may also be used.
All of the procedures involving experimental animals outlined below have been approved by the Animal Care Committee of the University of Manitoba, which is governed by the Canadian Council on Animal Care.
1. Procedure setup and preparation
NOTE: The following items are required for a tail vein catheter injection: insulin syringes, a 15 cm piece of PE10 tubing, 30 G needles, gauze, saline, forceps, green fluorescein dextran dye, pliers, and scissors. Also, have a needle ready with ketamine/xylazine anesthesia that will be injected before the imaging session.
CRITICAL: All materials and equipment in Steps 1 and 2 must be sterilized prior to the use by autoclaving or rinsing with 70% ethanol. If the pliers cannot be properly sterilized, the use of a pair of large needle holders is recommended. Catheter assembly must be done with pliers and forceps to avoid accidental needle punctures.
2. Tail vein injection
3. Two photon microscopy
4. Image Analysis
Fluorescein-dextran has a broad emission spectrum that can bleed through into the red channel, impacting RCaMP detection in ensheathing pericytes (Figure 2A). Spectral unmixing after data acquisition in the software program reduces the fluorescein bleeding through (Figure 2B, lower), enhancing calcium signal detection in subsequent analysis steps.
Calcium analysis with the image processing algorithms used in this protocol allows several different approaches to identify ROIs and intracellular calcium fluctuations (i.e., calcium signals). Selecting cellular structures by hand permits detection of calcium fluctuations within these regions (Figure 3A), including different types of signal peaks, such as single peaks and multi-peaks, after the normalized calcium traces are low-pass and band-pass filtered (Figure 3B). Additionally, ROIs are identified by grouping active pixels together where the fluorescence intensity changes over time using image processing algorithms developed by Ellefsen et al. 201416 and Barrett et al. 201817 (Figure 4). This can be applied to any dynamic cellular signal by adjusting the time, threshold and spatial parameters to encompass the expected size and shape of the signal. Decreasing the threshold for signal identification finds more regions of interest (Figure 4B).
Bright and clear hemodynamic kymographs can be analyzed to measure diameter and RBC velocity in blood vessels near ensheathing pericytes (Figure 5A, B). The diameter is calculated from the full width at half maximum of the fluorescence (Figure 5C). The RBC velocity is approximated from the streaks made from unlabeled RBCs, where the angle is input into a Radon transformation to calculate the velocity, flux (cells/s) and linear density (cells/mm; Figure 5D). Poor-quality kymographs where there is fluorescence saturation, poor signal to noise ratio or movement of the imaging field (Figure 6A) creates unreliable plots with error points (red crosses) where data cannot be determined (Figure 6B, C). The quality of the acquired data is critical for a good outcome and following the steps described in this protocol ensures good results.
Figure 1. Summary of protocol. The protocol presents the steps to acquire and analyze fluorescent calcium images from brain ensheathing pericytes and blood flow data from nearby blood vessels in anesthetized mice. The protocol is divided in 4 steps. 1) Procedure preparation: set up of equipment and catheter preparation; 2) Tail vein injection; 3) Data acquisition by two-photon microscopy; 4) Data analysis with image processing algorithms. Please click here to view a larger version of this figure.
Figure 2. Spectral unmixing of fluorophores. A) Representative average image of RCaMP ensheathing pericyte and fluorescein-dextran labelled blood vessel from a T-series acquisition. Scale bar= 10 µm. B) Upper: When considering individual channels, bleed-through from Channel 2 is apparent in Channel 1 (left). Lower: After spectral unmixing, the bleed-through is reduced and signal from RCaMP is more prominent in the pericyte structure. Please click here to view a larger version of this figure.
Figure 3. Hand selected ROIs and optimized calcium traces. A) Regions of interest selected in the used image processing software (rainbow shapes) can be used to identify calcium signal traces. B) Signal peaks from normalized traces are identified by low pass and band pass filtering the data. We defined the signal threshold as 3 times the standard deviation of the baseline period (first 30 frames) and any peaks over this threshold were considered a signal (lower trace). Please click here to view a larger version of this figure.
Figure 4. Automated, activity-based ROIs for calcium analysis. The same data was analyzed with a threshold of 7 times the standard deviation of the baseline (A) and 3 times the standard deviation of the baseline (B). Decreasing the threshold for identifying active pixels finds more ROIs (B) and signal peaks (pie chart) within the pericytes. Please click here to view a larger version of this figure.
Figure 5. Kymograph hemodynamic measurements. A) Example line scan through the vessel. B) Example of well-defined kymographs for diameter (left) and velocity (right). The black streaks within the right band of fluorescence correspond to RBCs. C) Diameter analysis with clear vasomotion fluctuations. D) Velocity analysis with plots for Y axis= RBC flux(cells/s), line density (cells/mm), velocity (mm/s), and streak angle (degree), signal to noise ratio (arbitrary units, a.u.), X axis=time (sec). Please click here to view a larger version of this figure.
Figure 6. Representation of Poor-Quality Hemodynamic measurements. A) Examples of poor-quality kymographs with fluorescence saturation, poor signal to noise ratio, or movement of the imaging field during acquisition. B and C) Plots similar to Figure 7 of diameter and velocity data that have error points (red dots) because of the poor-quality of the kymographs. (image E, Y axis=Diameter (µm), X axis=time (sec); image F, Y axis= RBC flux(cells/s), line density (cells/mm), velocity (mm/s), and streak angle (degree), signal to noise ratio (a.u.), X axis=time (sec). Please click here to view a larger version of this figure.
The present method provides details on mouse tail vein injection with a catheter, two-photon microscope image acquisition for depth stacks, cell calcium signaling movies, creation of hemodynamic kymographs, and calcium and hemodynamic analysis with our image processing algorithms17 (Figure 1). There are several advantages to these techniques that improve the in vivo imaging outcome and reduce time, resources, and animal stress during the session. First, the use of a catheter for tail vein injection provides more control over the needle, the syringe and the amount of substance injected into the circulation of the mouse. Additionally, it prevents dye injection into the tail tissue, saving expensive reagents. Second, we use transgenic mice which express genetically encoded calcium sensors in ensheathing pericytes and demonstrate how to localize them within the brain vascular network with a depth z-stack, which facilitates cell identification and relocation in subsequent imaging sessions long-term. This is an important factor in pericyte studies and ensures proper cell classification6,7. Third, we provide our parameters for collecting calcium movies and hemodynamic line scan data which are a good starting point for measuring dynamic cellular signals. Finally, we present our image processing algorithms17, a comprehensive image processing toolbox which contains multiple approaches for image pre-processing (such as spectral unmixing), calcium image analysis, and hemodynamic analysis (diameter, velocity, etc.). These algorithms can generate plots for a quick and easy visualization of the data, while minimizing the level of user expertise required to analyze results. Furthermore, it can be automated with a few lines of code to quickly batch process multiple datasets with the same parameters. This can potentially improve data visualization and the time investment of the researcher.
The key to collecting good calcium imaging data is to adjust the laser power and PMT settings for clear fluorescence signal acquisition, but to also collect data at a sufficient frame rate to capture the entire calcium event. The data in this protocol was acquired at 10-11 frames per second, which captures the slower calcium oscillations in ensheathing pericytes. There are also several steps during analysis that can improve the analysis outcome. First, spectral unmixing is beneficial if there is significant overlap between the emission spectra of fluorophores (Figure 2). Fluorescein-dextran was used in this protocol because it is a cost-effective and commercially available dextran conjugate that is commonly used for hemodynamic measurements5. Spectral unmixing helps to clean up the data for enhanced detection of calcium signals, but alternative fluorophores with narrower emission spectra could also be used. Second, hand selecting cellular structures as ROIs (Figure 3) is useful for classifying calcium events in different sub-cellular regions such as the soma or process branches. Activity-based ROI selection (Figure 4)16 provides more spatial and temporal information about individual calcium events. This can be helpful when determining the frequency of calcium events in a given area or the propagation of events to other cellular areas. The use of programming software to analyze imaging data can save researchers hours of time when data is batch-processed, but it requires some initial time investment to adjust the parameters for optimal results. The most important factors are the expected size (in µm2) of the active region as well as the duration of the signal (minimum signal time and maximum signal time must be defined). Researchers must examine some example T-series movies first to best determine which parameters fit their data. Finally, poor quality data acquired on the microscope can greatly hinder the analysis of calcium and hemodynamics (Figure 6). Therefore, care should be taken to optimize the microscope acquisition settings in the beginning. With these factors in mind, this protocol that can be adapted to fit calcium imaging or analysis of other dynamic cellular signals (e.g., fluorescent sodium, potassium, metabolite, or voltage fluctuations) in other tissues or cell types.
There are several limitations to this protocol. First, the data is collected under anesthesia, which affects brain activity and could impact blood flow. Similar imaging can be done in awake mice that are trained to accept head fixation for more physiological results. Additionally, it is important to remember that we collect 2-dimensional images of a 3-dimensional cell and blood vessel in vivo. Therefore, we can only capture a faction of the calcium events within these cells or the blood flow in a single section of blood vessel at a time.
Another limitation to note is that two-photon calcium imaging is sensitive to motion artifacts, where movement in and out of the focal plane can be mistaken for calcium fluctuations. This protocol was performed under anesthesia, which limits movement of the animal; however, motion artifacts can be introduced by the breathing rate of the mouse, heart rate, possible tissue swelling, and in the case of ensheathing pericytes, vessel contraction or vasomotion 4,6,18,19. Motion artifacts can be mitigated by several strategies. The image processing packages used in this protocol include an optional motion correction step, which utilizes a 2D convolution engine to align the images within the T-series based on the visible vasculature13,17. Frames with significant changes in the focal plane are identified by this algorithm and can be excluded from analysis. Additionally, it is possible to use statistical strategies within the imaging processing packages, such as a Z-score when generating the fluorescence traces to normalize the movement induced calcium fluctuations20. The most robust approach to account for motion artifacts in two-photon imaging is to combine expression of two fluorescent indicators within the same cell, such as a calcium indicator (e.g., GCaMP) and a fluorescent reporter (e.g., mCherry) that is calcium-independent. Fluctuations in the fluorescent reporter can then be attributed to movement and are subtracted from the calcium indicator signal to normalize motion artifacts.
The purpose of this protocol is to provide a clear understanding of how to collect optimal calcium imaging and blood flow data in vivo and to present new methods and analysis tools that researchers can implement in order to improve their results. These techniques can be applied to study the role of different pericytes populations in blood flow control or in different brain disease states. These imaging parameters can also be used to study calcium and blood flow in other cell types and organ systems and similar principles apply to other dynamic imaging techniques that are made possible by other genetically encoded sensors, beyond calcium.
J. Meza is supported by fellowships from Mitacs and Research Manitoba. Funding for this work was provided by Canadian Institutes for Health Research, Research Manitoba, Manitoba Medical Service Foundation, start-up funding from the University of Manitoba and Brain Canada through the Canada Brain Research Fund, with the financial support of Health Canada and the Azrieli Foundation. The views expressed herein do not necessarily represent the views of the Minister of Health or the Government of Canada.
Name | Company | Catalog Number | Comments |
Acta2-RCaMP1.07 | The Jackson Laboratory | 28345 | In the video protocol the animal model used is a female mouse of 10 months, 1 day old. |
Applicators (Regular) | Bisco | X-80250P | |
BioFormats package for MATLAB | NA | NA | Denominated in this protocol as "image processing packages". Available in: https://docs.openmicroscopy.org/bio-formats/ |
CHIPS MATLAB toolbox | NA | NA | Denomitaded in this protocol as "image processing algorithms". Barrett MJP, Ferrari KD, Stobart JL, Holub M, Weber B. CHIPS: an Extensible Toolbox for Cellular and Hemodynamic Two-Photon Image Analysis. Neuroinformatics. 2018;16(1):145-147. doi:10.1007/s12021-017-9344-y. Available in: https://github.com/EIN-lab/CHIPS |
Clear Ultrasound Gel, Medium viscosity | HealthCare Plus | UGC250 | |
Dextran, fluorescein, 70,000 MW, anionic | Thermo Fisher Scientific | D1823 | |
Dextran, Texas Red, 70,000 MW, neutral | Thermo Fisher Scientific | D1830 | |
Eye Lube Plus | Optixcare | NA | |
FIJI | Image J | NA | Denominated in this protocol as "image processor software". Available in: https://imagej.net/Fiji/Downloads |
GCaMP6sfl/fl | The Jackson Laboratory | ||
Head Post fixing platform | University of Zurich | NA | |
Ketamine (Narketan 100 mg/mL) | Vetoquinol | 440893 | |
MATLAB R2020b | NA | Denominated in this protocol as "programming platform ". Available in: https://www.mathworks.com/downloads/ | |
Needle 0.3mmx25mm | BD PrecisionGlide | 305128 | |
Objective XLUMPLFLN20XW | Olympus | NA | https://www.olympus-lifescience.com/en/objectives/lumplfln-w/ |
PDGFRβ-CreERT2 | The Jackson Laboratory | 30201 | |
Polyethylene Tubing, PE10 I.D. 28mm (0.11”) O.D. 61mm (.024”) | BD Intramedic | 427401 | |
Prairie View | Bruker Fluorescence Microscopy | NA | https://www.bruker.com/en/products-and-solutions/fluorescence-microscopy/multiphoton-microscopes/ultima-in-vitro.html |
Ultima In Vitro Multiphoton Microscope | Bruker Fluorescence Microscopy | NA | https://www.bruker.com/en/products-and-solutions/fluorescence-microscopy/multiphoton-microscopes/ultima-in-vitro.html |
Under Tank Heater | Reptitherm U.T.H | E169064 | |
Xylazine (Rompun 20 mg/mL) | Bayer HealthCare | 2169592 |
This article has been published
Video Coming Soon
关于 JoVE
版权所属 © 2024 MyJoVE 公司版权所有,本公司不涉及任何医疗业务和医疗服务。