## 2 Materials and Methods
### Control Samples
#### 2.1.4 Flow Cytometry Data Processing and Analysis  
Bivariate gating, an approach commonly used in flow cytometric analyses, was used primarily to analyze the multi-dimensional flow cytometry data [82; 83; 84; 85; 86; 87; 88]. FlowJo software (version 10.0.7, LLC, Ashland, OR, USA) was used in this study. Cell viability and compensation were applied during \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Marker** & **Fluorochrome** & **Neutrophil** & **Macrophage** &
\begin{tabular}{c} **Endothelial** \\ **Cell** \\ \end{tabular} & **Fibroblast** & **References** \\ \hline CD11b/c & FITC & + & + & - & - & [68; 70; 71] \\ CD29 & PE-Cy7 & + & + & + & + & [68; 71; 72; 73] \\ CD44H & APC-Cy7 & - & + & + & + & [68; 71; 73] \\ CD45 & PerCP-Cy5.5 & + & + & - & - & [68; 71; 72; 73; 74; 75] \\ CD68 & Red & + & + & - & + & [68; 71; 74; 76] \\ CD105 & PE & - & + & + & + & [68; 71; 73; 75] \\ CD106 & Prilliant & - & - & + & - & [68; 73] \\  & Violet 421 & - & - & + & - & [77; 78] \\  & His48 & APC & + & - & - & - & [77; 78] \\ Cell Viability & AmCyan & + & + & + & + & [79] \\ Dye & \multicolumn{1}{c}{} & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Rat cell surface marker profile for flow cytometry in Panel A. “+” denotes positive expression and “-” denotes negative expression. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Marker** & **Fluorochrome** & **Neutrophil** & **Macrophage** &
\begin{tabular}{c} **Endothelial** \\ **Cell** \\ \end{tabular} & **Fibroblast** & **References** \\ \hline CD31 & APC & + & + & + & - & [68; 71; 73] \\ CD45 & PerCP-Cy5.5 & + & + & - & - & [68; 71; 72; 73; 74; 75] \\ CD90 & FITC & - & - & + & **+** & [68; 71; 73; 75] \\ CD163 & PE-Cy7 & - & + & - & - & [68; 71; 75] \\ His48 & PE & + & - & - & - & [77; 78] \\  & AmCyan & + & + & + & + & [79] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Rat cell surface marker profile for flow cytometry in Panel B. “+” shows positive expression and “-” shows negative expression. the setup of the experiment. For all datasets, gating analysis was performed by one individual (AG) who was blind to experimental conditions. The gating strategy for Panel A first used FSC-A vs SSC-A to represent the distribution of cells based on size, granularity and intracellular composition. These two scattering parameters led to the exclusion of debris, other non-cellular particles and lymphocytes [89; 90]. To exclude doublets that were considered as single cells, three tests were implemented in the following order: (1) FSC-W vs FSC-H (W=width, H=height) to select low FSC-W cells, (2) FSC-W histogram to check the threshold and (3) FSC-H vs FSC-A (A=area) to select cells that were clustered diagonally [91; 92]. A positive gating approach was used in which, instead of excluding negative cells (showing negative expression of a marker), priority was given to cells showing positive expression of a marker. The CD29 vs CD45 plot was used to distinguish CD29+CD45+ hematopoietic cells (neutrophils and macrophages) from CD29+CD45- non-hematopoietic cells (endothelial cells and fibroblasts) [68; 71; 72; 73; 74; 75]. Here CD29 not only separated hematopoietic cells from non-hematopoietic cells, but it was also used as a conventional marker to gate out erythrocytes. For the gating of hematopoietic cells, CD45+His48+ cells were selected as neutrophils and were further verified using a His48+CD11b/c+ gates [77; 78]. From the same gate of hematopoietic cells, macrophages were identified by using CD106-CD44H+ and His48-CD68+ gates [68; 71; 74; 76]. In the case of non-hematopoietic cells (CD29+CD45-), CD106 was used to distinguish CD29+CD106+ endothelial cells from CD29+CD106- fibroblasts [68; 73]. Endothelial cells and fibroblasts were then confirmed by using CD44H+CD106+ and CD29+CD105+ gates, respectively [68; 71; 73; 75].