Microglial Metamorphosis in Three Dimensions in Virus Limbic Encephalitis: An Unbiased Pictorial Representation Based on a Stereological Sampling Approach of Surveillant and Reactive Microglia
Abstract
:1. Introduction
2. Experimental Procedures
2.1. Animals and Infection
2.2. Perfusion and Microtomy
2.3. Morphometry Based on 3D Reconstruction
2.4. Statistical Analysis of Morphometry
3. Results
Microglial Metamorphosis: From Surveillance to Reactivity to Virus Encephalitis
4. Discussion
4.1. Piry Virus Neuroinvasion and Microglial Response
4.2. Reactive Microglial Morphology
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Data Sharing
Additional Information
References
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Branched Structure Analysis | |
---|---|
Segment | Any portion of microglia branched structure with endings that are either nodes or terminations with no intermediate nodes |
Segments/mm | Number of segments/total length of the segments, expressed in millimeters |
No of trees | Number of trees in the microglia |
Total No of segments | Total number of segments in the tree |
Single branch length | Total length of the line segments used to trace the branch of interest |
Mean branch length (µm) | Mean = [Total length]/[Number of branches] |
Total branch length (µm) | Total length for all branches in the tree |
Tortuosity | Tortuosity = [Actual length of the segment] /[Distance between the endpoints of the segment]; smallest value is 1, which represents a straight segment; tortuosity allows segments of different lengths to be compared in terms of the complexity of the paths they take |
Mean branch surface area (µm2) | Computed by modeling each branch as a frustum (truncated right circular cone) divided by the number of branches |
Total tree surface area (µm2) | Two-dimensional (2D) surface area of a microglia arbor computed based on the area defined by the endpoints of all trees |
Branch volume (µm3) | Computed by modeling each piece of each branch as a frustum |
Total branch volume | Total volume for all branches in the tree |
Base diameter of primary branch (µm) | Diameter at the start of the first segment |
Planar angle | Computed based on the endpoints of the segments; references the change in direction of a segment relative to the previous segment |
Fractal dimension k-dim | The “k-dim” of the fractal analysis, describing how the structure of interest fills space; significant statistical differences in k-dim suggest morphological dissimilarities |
Convex hull: perimeter (µm), area (µm2), 2D surface area (µm2), 3D or volume (µm3) | Convex hull measures the size of the branching field by interpreting a branched structure as a solid object controlling a given amount of physical space; the amount of physical space is defined in terms of convex-hull volume, surface area, area, and/or perimeter. |
Vertex analysis | Describes the overall structure of a branched object based on topological and metrical properties. Root (or origin) point: For neurons, microglia or astrocytes, the origin is the point at which the structure is attached to the soma. Main types of vertices: Vd (bifurcation) or Vt (trifurcation), nodal (or branching) points. Vp: Terminal (or pendant) vertices. Va: primary vertices connecting 2 pendant vertices; Vb: secondary vertices connecting 1 pendant vertex (Vp) to 1 bifurcation (Vd) or 1 trifurcation (Vt); Vc: tertiary vertices connecting either 2 bifurcations (Vd), 2 trifurcations (Vt), or 1 bifurcation (Vd) and 1 trifurcation (Vt). In the present report, we measured the number of vertices Va, Vb, and Vc. |
Complexity | Complexity = [Sum of the terminal orders + Number of terminals] × [Total branch length/Number of primary branches] |
Descriptive Results | |||||||||
---|---|---|---|---|---|---|---|---|---|
N | Mean | Std. Deviation | Std. Error | 95% Confidence Interval for Mean | Minimum | Maximum | |||
Lower Bound | Upper Bound | ||||||||
Tortuosity | 1 | 75 | 1.33 | 0.12 | 0.01 | 1.30 | 1.36 | 1.09 | 1.56 |
2 | 30 | 1.35 | 0.09 | 0.02 | 1.31 | 1.38 | 1.18 | 1.59 | |
3 | 106 | 1.20 | 0.09 | 0.01 | 1.18 | 1.21 | 1.08 | 1.66 | |
Total | 211 | 1.27 | 0.12 | 0.01 | 1.25 | 1.28 | 1.08 | 1.66 | |
Total branch volume (µm3) | 1 | 75 | 381.99 | 131.21 | 15.15 | 351.80 | 412.17 | 194.82 | 840.86 |
2 | 30 | 696.56 | 202.32 | 36.94 | 621.01 | 772.11 | 286.29 | 1167.60 | |
3 | 106 | 104.39 | 52.92 | 5.14 | 94.20 | 114.58 | 18.69 | 234.52 | |
Total | 211 | 287.26 | 239.03 | 16.46 | 254.82 | 319.70 | 18.69 | 1167.60 | |
Mean branch volume (µm3) | 1 | 75 | 2.79 | 1.13 | 0.13 | 2.53 | 3.05 | 1.00 | 6.62 |
2 | 30 | 3.34 | 1.36 | 0.25 | 2.83 | 3.85 | 1.07 | 6.52 | |
3 | 106 | 1.22 | 0.55 | 0.05 | 1.12 | 1.33 | 0.40 | 3.28 | |
Total | 211 | 2.08 | 1.28 | 0.09 | 1.91 | 2.25 | 0.40 | 6.62 | |
Convex hull surface (µm2) | 1 | 75 | 1768.35 | 392.52 | 45.32 | 1678.04 | 1858.66 | 1133.26 | 2677.56 |
2 | 30 | 2882.78 | 473.73 | 86.49 | 2705.89 | 3059.67 | 1362.55 | 3689.12 | |
3 | 106 | 689.23 | 247.31 | 24.02 | 641.60 | 736.86 | 170.98 | 1186.62 | |
Total | 211 | 1384.68 | 856.26 | 58.95 | 1268.48 | 1500.89 | 170.98 | 3689.12 | |
Complexity | 1 | 75 | 945,72.82 | 53,188.84 | 6141.72 | 82,335.18 | 106,810.46 | 10,038.40 | 250,383.00 |
2 | 30 | 261,064.33 | 117,350.51 | 21,425.17 | 217,244.93 | 304,883.73 | 101,354.00 | 568,527.00 | |
3 | 106 | 31,720.23 | 21,318.02 | 2070.59 | 27,614.64 | 35,825.83 | 1451.06 | 92,769.60 | |
Total | 211 | 86,669.37 | 94,958.17 | 6537.19 | 73,782.44 | 99,556.29 | 1451.06 | 568,527.00 |
One-Way ANOVA | ||||||
---|---|---|---|---|---|---|
Sum of Squares | df | Mean Square | F | Sig. | ||
Tortuosity | Between groups | 1.01 | 2.00 | 0.50 | 51.64 | 0.00 |
Within groups | 2.03 | 208.00 | 0.01 | |||
Total | 3.03 | 210.00 | ||||
Total branch volume (µm3) | Between groups | 9,243,466.58 | 2.00 | 4,621,733.29 | 348.92 | 0.00 |
Within groups | 2,755,100.53 | 208.00 | 13,245.68 | |||
Total | 11,998,567.12 | 210.00 | ||||
Mean branch volume (µm3) | Between groups | 163.21 | 2.00 | 81.61 | 94.20 | 0.00 |
Within groups | 180.20 | 208.00 | 0.87 | |||
Total | 343.41 | 210.00 | ||||
Convex hull surface (µm2) | Between groups | 129,635,754.77 | 2.00 | 64,817,877.38 | 554.10 | 0.00 |
Within groups | 24,331,445.08 | 208.00 | 116,978.10 | |||
Total | 153,967,199.85 | 210.00 | ||||
Complexity | Between groups | 1,237,150,125,189.31 | 2.00 | 618,575,062,594.66 | 196.00 | 0.00 |
Within groups | 656,431,092,622.02 | 208.00 | 3,155,918,714.53 | |||
Total | 1,893,581,217,811.34 | 210.00 |
Multiple Comparisons | ||||||||
---|---|---|---|---|---|---|---|---|
Dependent Variable | Between-Groups Comparisons | Mean Difference (I-J) | Std. Error | Sig. | 95% Confidence Interval | |||
Lower Bound | Upper Bound | |||||||
Tortuosity | Tukey HSD | 1 vs. | 2 | −0.017 | 0.021 | 0.710 | −0.067 | 0.034 |
3 | 0.13291 * | 0.015 | 0.000 | 0.098 | 0.168 | |||
2 | 1 | 0.017 | 0.021 | 0.710 | −0.034 | 0.067 | ||
3 | 0.14976 * | 0.020 | 0.000 | 0.102 | 0.198 | |||
3 | 1 | −0.13291 * | 0.015 | 0.000 | −0.168 | −0.098 | ||
2 | −0.14976 * | 0.020 | 0.000 | −0.198 | −0.102 | |||
Total branch volume (µm3) | Tukey HSD | 1 vs. | 2 | −314.57115 * | 24.862 | 0.000 | −373.261 | −255.881 |
3 | 277.59531 * | 17.366 | 0.000 | 236.602 | 318.589 | |||
2 | 1 | 314.57115 * | 24.862 | 0.000 | 255.881 | 373.261 | ||
3 | 592.16647 * | 23.801 | 0.000 | 535.982 | 648.351 | |||
3 | 1 | −277.59531 * | 17.366 | 0.000 | −318.589 | −236.602 | ||
2 | −592.16647 * | 23.801 | 0.000 | −648.351 | −535.982 | |||
Mean branch volume (µm3) | Tukey HSD | 1 vs. | 2 | −0.55013 * | 0.201 | 0.018 | −1.025 | −0.075 |
3 | 1.56653 * | 0.140 | 0.000 | 1.235 | 1.898 | |||
2 | 1 | 0.55013 * | 0.201 | 0.018 | 0.075 | 1.025 | ||
3 | 2.11666 * | 0.192 | 0.000 | 1.662 | 2.571 | |||
3 | 1 | −1.56653 * | 0.140 | 0.000 | −1.898 | −1.235 | ||
2 | −2.11666 * | 0.192 | 0.000 | −2.571 | −1.662 | |||
Convex hull surface (µm2) | Tukey HSD | 1 vs. | 2 | −1114.43247 * | 73.885 | 0.000 | −1288.846 | −940.019 |
3 | 1079.11491 * | 51.607 | 0.000 | 957.291 | 1200.939 | |||
2 | 1 | 1114.43247 * | 73.885 | 0.000 | 940.019 | 1288.846 | ||
3 | 2193.54738 * | 70.731 | 0.000 | 2026.579 | 2360.515 | |||
3 | 1 | −1079.11491 * | 51.607 | 0.000 | −1200.939 | −957.291 | ||
2 | −2193.54738 * | 70.731 | 0.000 | −2360.515 | −2026.579 | |||
Complexity | Tukey HSD | 1 vs. | 2 | −166,491.51467 * | 12,135.741 | 0.000 | −195,139.323 | −137,843.707 |
3 | 62,852.58386 * | 8476.540 | 0.000 | 42,842.739 | 82,862.429 | |||
2 | 1 | 166,491.51467 * | 12,135.741 | 0.000 | 137,843.707 | 195,139.323 | ||
3 | 229,344.09852 * | 11,617.664 | 0.000 | 201,919.271 | 256,768.926 | |||
3 | 1 | −62,852.58386 * | 8476.540 | 0.000 | −82,862.429 | −42,842.739 | ||
2 | −229,344.09852 * | 11,617.664 | 0.000 | −256,768.926 | −201,919.271 |
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da Silva Creão, L.S.; Neto, J.B.T.; de Lima, C.M.; dos Reis, R.R.; de Sousa, A.A.; dos Santos, Z.A.; Diniz, J.A.P.; Diniz, D.G.; Diniz, C.W.P. Microglial Metamorphosis in Three Dimensions in Virus Limbic Encephalitis: An Unbiased Pictorial Representation Based on a Stereological Sampling Approach of Surveillant and Reactive Microglia. Brain Sci. 2021, 11, 1009. https://doi.org/10.3390/brainsci11081009
da Silva Creão LS, Neto JBT, de Lima CM, dos Reis RR, de Sousa AA, dos Santos ZA, Diniz JAP, Diniz DG, Diniz CWP. Microglial Metamorphosis in Three Dimensions in Virus Limbic Encephalitis: An Unbiased Pictorial Representation Based on a Stereological Sampling Approach of Surveillant and Reactive Microglia. Brain Sciences. 2021; 11(8):1009. https://doi.org/10.3390/brainsci11081009
Chicago/Turabian Styleda Silva Creão, Leonardo Sávio, João Bento Torres Neto, Camila Mendes de Lima, Renata Rodrigues dos Reis, Aline Andrade de Sousa, Zaire Alves dos Santos, José Antonio Picanço Diniz, Daniel Guerreiro Diniz, and Cristovam Wanderley Picanço Diniz. 2021. "Microglial Metamorphosis in Three Dimensions in Virus Limbic Encephalitis: An Unbiased Pictorial Representation Based on a Stereological Sampling Approach of Surveillant and Reactive Microglia" Brain Sciences 11, no. 8: 1009. https://doi.org/10.3390/brainsci11081009