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. 2017 Oct 31;7(1):14444.
doi: 10.1038/s41598-017-14305-z.

Determining the contents and cell origins of apoptotic bodies by flow cytometry

Lanzhou Jiang  1 , Stephanie Paone  1 , Sarah Caruso  1 , Georgia K Atkin-Smith  1 , Thanh Kha Phan  1 , Mark D Hulett  1 , Ivan K H Poon  2
Affiliations

Determining the contents and cell origins of apoptotic bodies by flow cytometry

Lanzhou Jiang et al. Sci Rep. .

Abstract

Over 200 billion cells undergo apoptosis every day in the human body in order to maintain tissue homeostasis. Increased apoptosis can also occur under pathological conditions including infection and autoimmune disease. During apoptosis, cells can fragment into subcellular membrane-bound vesicles known as apoptotic bodies (ApoBDs). We recently developed a flow cytometry-based method to accurately differentiate ApoBDs from other particles (e.g. cells and debris). In the present study, we aim to further characterize subsets of ApoBDs based on intracellular contents and cell type-specific surface markers. Utilizing a flow cytometry-based approach, we demonstrated that intracellular contents including nuclear materials and mitochondria are distributed to some, but not all ApoBDs. Interestingly, the mechanism of ApoBD formation could affect the distribution of intracellular contents into ApoBDs. Furthermore, we also showed that ApoBDs share the same surface markers as their cell of origin, which can be used to distinguish cell type-specific ApoBDs from a mixed culture. These studies demonstrate that ApoBDs are not homogeneous and can be divided into specific subclasses based on intracellular contents and cell surface markers. The described flow cytometry-based method to study ApoBDs could be used in future studies to better understand the function of ApoBDs.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Electronic gating strategy for analysis of DNA content in ApoBDs generated from apoptotic Jurkat T cells. Flow cytometry analysis showing the six-stage electronic gating strategy used to identify ApoBDs with different amount of DNA from a sample containing a mixture of viable cells, apoptotic cells, necrotic cells, cell debris and ApoBDs. The final percentages of ApoBDs containing different amount of DNA relative to all the cells/particles in the sample are included on the flow cytometry plots (bold). The gating of Hoechst 33342 low events is determined based on unstained sample. Data are representative of at least three independent experiments.
Figure 2
Figure 2
Electronic gating strategy for analysis of mitochondrial content in ApoBDs generated from apoptotic Jurkat T cells. Flow cytometry analysis showing the six-stage electronic gating strategy used to identify ApoBDs with different amount of mitochondria from a sample containing a mixture of viable cells, apoptotic cells, necrotic cells, cell debris and ApoBDs. The final percentages of ApoBDs containing different amount of mitochondria relative to all the cells/particles in the sample are included on the flow cytometry plots (bold). The gating of MitoTracker Green low events is determined based on unstained sample. Data are representative of at least three independent experiments.
Figure 3
Figure 3
Distribution of intracellular contents into ApoBDs is altered by the mechanism of apoptotic cell disassembly. (a) Top, schematic of Jurkat T cells undergoing apoptotic cell disassembly and the distribution of intracellular contents (DNA and mitochondria) when treated with trovafloxacin (PANX1 inhibitor) or a combination of trovafloxacin and GSK 269962 (ROCK1 inhibitor) during apoptosis. Bottom, schematic of THP-1 monocytes undergoing apoptotic cell disassembly and the distribution of intracellular contents during this process. (b) Jurkat T cells treated with trovafloxacin (20 μM) alone or in combination with GSK 269962 (1 μM) to modulate the mechanism of ApoBD formation. The distribution of DNA and mitochondria into ApoBDs quantified based on ApoBD DNA distribution index and ApoBD mitochondria distribution index, respectively (n = 3). ApoBD DNA distribution index = DNA+ ApoBDs/DNA− ApoBDs; ApoBD mitochondria distribution index = mitochondria+ ApoBDs/mitochondria− ApoBDs. (c) Jurkat T cells were treated with trovafloxacin (20 μM) to modulate the mechanism of ApoBD formation, and the size of ApoBDs (based on forward scatter, FSC) and distribution of mitochondria into ApoBDs (based on ApoBD mitochondria distribution index) were determined for ApoBDs that contain a substantial amount (DNA+), or no or very low amount of DNA (DNA−) (n = 3) (d) Comparison of the distribution of DNA and mitochondria into ApoBDs generated from apoptotic Jurkat T cells and THP-1 monocytes (n = 3). (b–d) Jurkat T cells and THP-1 monocytes were treated with UV to induce apoptosis. Data are representative of at least three independent experiments. *P < 0.05, **P < 0.01, ***P < 0.001, unpaired Student’s two-tailed t-test.
Figure 4
Figure 4
DNA and RNA are not separated into different ApoBDs. (a) Flow cytometry analysis showing the electronic gating strategy used to monitor the distribution of DNA and RNA into ApoBDs derived from apoptotic Jurkat T cells. Cells were stained with a combination of SYTO RNAselect, Hoechst 33342 and A5-APC. The gating of RNA− DNA− ApoBDs is determined based on unstained sample. (b) Left, bar chart and right, pie chart showing the percentage of RNA+DNA+, RNA+DNA−, RNA-DNA+, and RNA−DNA− ApoBDs derived from UV treated Jurkat T cells (n = 3). (c) Jurkat T cells were treated with trovafloxacin (20 μM) alone or in combination with GSK 269962 (1 μM) to modulate the mechanism of ApoBD formation. The distribution of RNA and DNA into ApoBDs monitored as per (a) (n = 3). (d) The percentage of RNA+ and RNA+DNA+ ApoBDs are compared between different treatment groups as per (c) (n = 3). (a–d) Jurkat T cells were induced to undergo apoptosis by UV treatment. RNA+ and DNA+ ApoBDs represent ApoBDs containing a substantial amount of RNA and DNA, respectively. RNA− and DNA− ApoBDs represent ApoBDs containing no or very low amount of RNA and DNA, respectively. Data are representative of at least three independent experiments. *P < 0.05, **P < 0.01, ***P < 0.001, unpaired Student’s two-tailed t-test.
Figure 5
Figure 5
Establishing optimal surface cell markers on endothelial cells, monocytes and T cells. Representative histograms show surface expression of (a) CD31, CD146, CD45 on viable HUVEC, THP-1 monocytes and Jurkat T cells and (b) CD3 and CD11b expression on viable THP-1 monocytes and Jurkat T cells. Data are representative of at least three independent experiments.
Figure 6
Figure 6
Changes in the expression of cell surface markers during apoptosis and cell disassembly. (a) Representative histograms showing the expression of cell surface markers CD146, CD11b, CD3 and CD45 on viable cells, apoptotic cells and ApoBDs from HUVEC, THP-1 monocytes and Jurkat T cells. (b) Electronic gating strategy for the identification of viable cells, apoptotic cells and ApoBDs derived from HUVEC, THP-1 monocytes and Jurkat T cells. Data are representative of at least three independent experiments.
Figure 7
Figure 7
Identification of cell type specific ApoBDs in mixed culture by flow cytometry. (a) Electronic gating strategy used to identify viable endothelial cells, monocytes and T cells based on CD146, CD45, CD3 and CD11b surface expression. (b) Electronic gating strategy used to identify apoptotic cells and ApoBDs from endothelial cells, monocytes and T cells based on CD146, CD45, CD3 and CD11b surface expression. Data are representative of at least three independent experiments.
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