Aby wyświetlić tę treść, wymagana jest subskrypcja JoVE. Zaloguj się lub rozpocznij bezpłatny okres próbny.
Method Article
This article provides detailed methodology to identify and quantify functional T lymphocyte subsets present within murine kidney, aorta and lymph nodes by intracellular staining and flow cytometry. The model of angiotensin II induced hypertension was chosen to explain, step-by-step, the procedures and fundamental principles of flow cytometry and intracellular staining.
It is now well known that T lymphocytes play a critical role in the development of several cardiovascular diseases1,2,3,4,5. For example, studies from our group have shown that hypertension is associated with an excessive accumulation of T cells in the vessels and kidney during the development of experimental hypertension6. Once in these tissues, T cells produce several cytokines that affect both vascular and renal function leading to vasoconstriction and sodium and water retention1,2. To fully understand how T cells cause cardiovascular and renal diseases, it is important to be able to identify and quantify the specific T cell subsets present in these tissues. T cell subsets are defined by a combination of surface markers, the cytokines they secrete, and the transcription factors they express. The complexity of the T cell population makes flow cytometry and intracellular staining an invaluable technique to dissect the phenotypes of the lymphocytes present in tissues. Here, we provide a detailed protocol to identify the surface and intracellular markers (cytokines and transcription factors) in T cells isolated from murine kidney, aorta and aortic draining lymph nodes in a model of angiotensin II induced hypertension. The following steps are described in detail: isolation of the tissues, generation of the single cell suspensions, ex vivo stimulation, fixation, permeabilization and staining. In addition, several fundamental principles of flow cytometric analyses including choosing the proper controls and appropriate gating strategies are discussed.
Recent evidence demonstrates that the adaptive immune system, particularly T lymphocytes, play a critical role in the development of several cardiovascular diseases1,2,3,4,5. For example, in the model of angiotensin II induced hypertension, an accumulation of T cells in the vessels and kidneys of mice has been described6. The vascular accumulation is predominantly in the adventitia and the perivascular fat. In the kidney, T cells accumulate in both the medulla and renal cortex. Depending on which subset is involved, these T cells give rise to different cytokines that can affect vascular and renal function and lead to the development of pathology (reviewed by McMaster et al.6).
CD4+ T helper lymphocytes can be divided into several subsets: T helper 1 (Th1), Th2, Th9, Th17, Th22, T regulatory (Treg) cells, and T follicular helper (Tfh) cells based on their functions and signature cytokines7. Similarly, CD8+ cytotoxic T cells can be classified as Tc1, Tc2, Tc17 or Tc98. There are also double negative T cells (i.e. cells that do not express the CD4 or CD8 T cell markers). A subset of these cells possess an alternate gamma delta T cell receptor (instead of the classical alpha and beta receptors) and are therefore referred to as gamma delta T cells. The multi-parameter analysis by flow cytometry of surface marker, cytokine and transcription factor constitutes the best approach to identify these cells. Although this method is extensively used in the field of immunology, it is less well described in solid organs and in the setting of cardiovascular diseases.
Historically, the identification of lymphocytes in tissues was limited to immunohistochemistry or RT-PCR approaches. Although immunohistochemistry and immunofluorescence are powerful methods to determine the tissue distribution of an antigen of interest, they are inadequate to phenotypically identify the subsets involved. In addition, while RT-PCR analysis is useful to detect mRNA expression of antigens, cytokines or transcription factors, it doesn't allow the detection of multiple proteins simultaneously at the level of individual cells.
The advent of flow cytometry, especially when combined with intracellular staining to detect cytokines and transcription factors, provides investigators with a powerful technique that allows identification and quantification at the single cell level of immune cell subsets in solid organs. We have optimized an intracellular staining assay to identify by flow cytometry the major T cell subsets present within murine kidney, aorta and aortic draining lymph nodes in a model of angiotensin II induced hypertension. The optimization of each step: tissue digestion, ex vivo activation, permeabilization, and surface and intracellular staining results in a highly reproducible assay that can be applied to other cardiovascular and renal disease models.
Vanderbilt University's Institutional Animal Care and Use Committee has approved the procedures described herein. Mice are housed and cared for in accordance with the Guide for the Care and Use of Laboratory Animals (National Academies Press. Revised 2010).
1. Isolation of the Aortic Draining Lymph Nodes, Kidney and Aorta from Mice
2. Generation of Single Cell Suspensions from Each Tissue
3. Ex Vivo Stimulation for Cytokine Detection by Flow Cytometry
4. Surface Staining
5. Fixation, Permeabilization and Intracellular Staining
6. Compensation, Gating, Normalization, and Tips
The protocol described permits the identification of surface and intracellular markers in T cells isolated from murine kidney, aorta and aortic draining lymph nodes in a model of angiotensin II induced hypertension. Representative results are presented below.
Figure 1 demonstrates the gating strategy used to identify the T cell population in a single cell suspension prepared from the aorta of a WT mouse infused ...
The protocol described herein has been optimized to properly identify T cell subsets present within murine kidneys, aorta and lymph nodes. This protocol can be easily adapted to examine other immune cell subsets such as B lymphocytes and innate immune cells and can be modified to include other tissue types. The digestion step is critical and has to be modified and optimized for each tissue9. A prolonged digestion step or the use of an inappropriate enzyme can affect the stability of antigen expression. Similar...
MSM is supported by a grant from Gilead Cardiovascular Sciences.
This work was supported by an American Heart Association Fellowship Award (16POST29950007) to FL, a training grant from the National Institutes of Health (NIH T32 HL069765) to BLD, an American Heart Association Fellowship Award (14POST20420025) to MA Saleh, and an NIH K08 award (HL121671) to MSM. MSM is also supported by a research grant from Gilead Sciences, Inc.
Name | Company | Catalog Number | Comments |
Collagenase D | ROCHE | 11088882001 | |
Collagenase A | ROCHE | 10103586001 | |
Collagenase B | ROCHE | 11088815001 | |
Dnase | ROCHE | 10104159001 | |
1x Red blood cell lysis buffer | eBioscience | 00-4333-57 | |
RPMI Medium 1614 1x | Gibco | 11835-030 | |
DPBS without calcium and magnesium | Gibco | 14190-144 | |
Percoll | GE Healthcare | 17-5445-02 | For density gradient centrifugation |
GentleMACS™ C tube | Miltenyi Biotec | 130-096-334 | |
GentleMACS dissociator device | Miltenyi Biotec | 130-093-235 | Use the program SPLEEN_04 |
Cell activation cocktail (with Brefeldin A) | Biolegend | 423303 | |
anti-CD16/32 | eBioscience | 14-0161-81 | dilute 1:100 |
LIVE/DEAD fixable violet dead cell stain kit | Life Technologies | L34955 | |
Transcription factor buffer set | BD Pharmingen | 562725 | |
OneComp eBeads | eBioscience | 01-1111-42 | |
123 count eBeads | eBioscience | 01-1234-42 | |
CD45 AmCyan (clone 30-F11) | BioLegend | 103138 | |
CD3 PerCP-Cy5.5 (clone 17A2) | BioLegend | 100218 | |
IL-17A FITC (clone TC11-18H10.1) | BioLegend | 506910 | |
IL-17F APC (clone 9D3.1C8) | BioLegend | 517004 | |
CD4 APC-Cy7 (clone GK1.5) | BD Biosciences | 560181 | |
CD8 APC (clone 53-67) | eBioscience | 17-0081-82 | |
T-bet PE-Cy7 (clone 4B10) | BioLegend | 644823 | |
IFNγ FITC (clone XMG1.2) | BD Biosciences | 557724 |
Zapytaj o uprawnienia na użycie tekstu lub obrazów z tego artykułu JoVE
Zapytaj o uprawnieniaThis article has been published
Video Coming Soon
Copyright © 2025 MyJoVE Corporation. Wszelkie prawa zastrzeżone