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The STEM class at Mass Academy is a project-based based course where students learn and implement the scientific research process.

Investigating T Cell Exhaustion in COVID-19 Infected Patients Via Flow Cytometry Analysis

Overview: This project aimed to analyze and compare the phenotypes of T cells from healthy donors and hospitalized COVID-19 patients to determine if T cells infected with COVID-19 become dysregulated after acute infection. After flow cytometry analysis of healthy and COVID-19 infected T cells, it was determined that both the CD8+ and CD4+ T cells of the COVID-19 patients showed signs of dysregulation.

ABSTRACT

There are two epigenetic possibilities for T cells that have been exposed to chronic antigen stimulation: to become exhausted or become a memory T cell. Exhausted T cells are unable to attack antigens, activate other cells, and T cell exhaustion can be characterized by elevated levels of inhibitory receptors and transcription factors associated with exhaustion, and low levels of cytokines.

“Long COVID” is a condition in which COVID-19 symptoms reoccur weeks or even months after the initial infection. These patients often test negative for COVID-19 with PCR tests, yet they continue to experience the effects of a COVID-19 infection. An inability to recover from an infection often indicates a weakened immune system. If the T cells of these COVID-19 patients became dysregulated after acute infection, they would be unable to activate and attack the antigen.

To investigate T cell exhaustion development and the phenotypes of T cells throughout a COVID-19 infection, raw flow cytometry data from the research article “Mucosal-associated invariant T cell responses differ by sex in COVID-19” was collected from FlowRepository, a database for flow cytometry data. In this study, PBMC samples were collected from 10 healthy donors, 44 hospitalized and 20 non-hospitalized individuals with confirmed COVID-19 infections, and 9 close contacts of those infected with COVID-19. These cells were stained to detect surface molecule expression. The expression levels of surface molecules CD38, PD-1, CD127 were analyzed using FloJo analysis.

There was no significant change in CD-127 levels, meaning that the T cells’ function did not change, yet there was a significant difference in the PD-1 levels of the healthy and infected CD4+ T cells. Additionally, there was a significant change between the CD38 levels of the healthy and infected CD8+ T cells. Although, the T cells did not lose functionality, they presented with significant levels of exhaustion markers. This could indicate that acute COVID-19 infections cause T cells to become dysregulated, halting the recovery process.

Graphical Abstract
Graphical Abstract
Research Proposal

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Literature Review

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Phrase 1

Do T cells infected with COVID-19 become dysregulated after acute infection?

Phrase 2

It is hypothesized that the T cells from hospitalized COVID-19 patients will have significantly higher levels of exhaustion markers PD-1 and CD38 and significantly lower levels of cytokine CD127.

Background Infographic

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Background

Cancer and chronic viruses often extensively damage the human body, and many variants are difficult or impossible to treat successfully. While cancer turns healthy cells into dysregulated tumor cells, viruses act as foreign agents that replicate and attack the body. Despite their differences, both tumors and viruses have substantial effects on the immune system. Even if these conditions are treated, they can damage the immune system, often permanently.

T cells are part of the immune system’s defense against these illnesses. Their function is to store memories of different viruses and cancers, attack infected or cancerous cells, activate other immune cells, produce cytokines (cell-signaling molecules), and perform other regulatory tasks (Britannica, T. Editors of Encyclopaedia, 2020). Since T cells are an integral part of the immune system, chronic exposure to viruses and/or cancers can lead to the cells becoming damaged or “exhausted.”

T cell exhaustion is a state in which these cells lose functionality. Exhausted T cells cannot remember viruses and cancers or fight against them, compromising the immune system’s defense against illnesses. However, this is not the full extent of the damage; chronic stimulation results in epigenetic scarring, which often permanently alters T cells (Penn Medicine, 2021).

According to the National Human Genome Research Institute (n.d), Epigenetic changes are heritable phenotypic alterations that are caused when genes are activated or deactivated. Unlike genetic changes, epigenetic changes do not affect a person’s genes. When T cells become exhausted, they phenotypically change, meaning that they are epigenetically altered.

While T cell exhaustion commonly occurs after chronic infection and long-term cancer, COVID-19 patients have been experiencing similar symptoms after acute infection. A 2020 report from Italy found that 87% of COVID-19 patients who had recovered and been discharged from the hospital experienced at least one persisting symptom 60 days after discharge (Carfi et al. 2020). “Long COVID” is a condition in which COVID-19 symptoms reoccur weeks or even months after the initial infection. These patients often test negative for COVID-19 with PCR tests, yet they continue to experience fatigue, dyspnea, joint pain, chest pain, cough, headache, diarrhea, and general worsened quality of life (Raveendran et al. 2021).

An inability to recover from an infection often indicates a weak immune system. If the T cells of these COVID-19 patients became dysregulated after acute infection, they would be unable to activate other T cells, attack the COVID-19 antigens, and store memory of the antigen in the immune system. Dysregulated T cells create a weak immune system, which would cause the prolonged COVID-19 symptoms, which are associated with “Long COVID.” Analyzing T cells from donors infected with COVID-19 could determine whether the virus induces dysregulation in T cells.

Procedure Infographic

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Procedure

Raw flow cytometry data was collected from the FlowRepository database, sourced from the research article “Mucosal-associated invariant T cell responses differ by sex in COVID-19” In this study, PBMC cell samples were collected from 10 healthy, control donors, 44 hospitalized and 20 non-hospitalized individuals with confirmed COVID-19 infections, and nine close contacts of those infected with COVID-19. These cells were extracted and stained to detect surface expression of CD27, CD38, CD127, HLA-DR, CD1c, CD141, CD45ra, CD16, CCR5, CD4, CD11c, CD56, CD8, CCR7, IgD, CD3, IgM, IgG, CD28, CCR6, CXCR5, PD1, CD57, CD25, CD95, CXCR3, CD24, CD20, CD45, CD11b, TCR gd, CD14, CD19, CD123, and CD161.

All flow cytometry data was analyzed using FloJo version 10.8.1. All cell populations were first analyzed with side scatter area (SSC-A) against forward scatter area (FSC-A). Side scatter measures the granularity of a cell, while forward scatter measures cell size. By comparing the two, cell populations based on groups with similar size and granularity can be determined and gated. In this project, several different populations were selected and gated together (As seen in Figure 1). This was then followed up with additional analyses to determine the type of cells in each population.

Then, forward scatter height (FSC-H) was plotted against FSC-A to determine if each cell population consisted of singlets, whose population was gated (As seen in Figure 2). This excludes doublets, which are two or more cells clumped together. Doublets can include cells that are both positive and negative for a certain stain, so it is essential to exclude them from the analysis.

The next step was to determine which cells were viable. The cells stained for the Live/Dead stain were plotted against SSC-A to determine the amount of dead cells. Cells that were negative for the Live/Dead stain were gated off as viable cells.

Then, the CD8+ and CD4+ T cell populations were determined by plotting the BUV615-A stain (CD4) and the BUV805-A stain (CD4) against each other (As seen in Figure 4). CD8+ T cells are cytotoxic, and they are responsible for fighting antigens and toxic cells. CD4+ T cells are able to call for CD8+ cells when antigens are present and differentiate into other types of T cells. Both CD4+ and CD8+ are essential for the immune system’s defense against viruses, so their populations were chosen for analysis.

The identified T cell populations were then gated for PD-1, CD38, and CD127 positivity (As seen in Figure 5). PD-1 and CD-38 are exhaustion markers, so populations with higher levels of these molecules are likely to be dysregulated. CD127 is a cytokine, so high levels of this molecule would indicate a functioning T cell. Therefore, analysis of PD-1, CD38, and CD127 positivity help determine the functionality of a T cell population.

The gating strategy was applied to PBMC samples from healthy donors and hospitalized patients infected with COVID-19. The samples from individuals exposed to COVID-19 and those who are not hospitalized yet have a COVID-19 infection were not used for analysis since these samples would not reflect the consequences of a severe COVID-19 infection.

Figure 1

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Figure 2

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Figure 3

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Analysis

After analysis of all of the healthy and COVID-19 infected samples, the percentages of CD4+PD1+, CD8+PD1+, CD4+CD38+, CD8+CD38+, CD4+CD127+, and CD8+CD127+ cells were compared (See Figure 3).

The data from table one was first analyzed using a Welch’s t-test. When the healthy and hospitalized sample CD4+PD1+ percentage values were compared, there was a p-value of *0.028. A comparison between the healthy and hospitalized CD8+PD1+ percentage values yielded a p-value of 0.26. The CD4+CD127+ percentage values from the healthy and hospitalized samples were compared to find a p-value of 0.15. Furthermore, the comparison between healthy and hospitalized CD8+CD127+ percentage values yielded a p-value of 0.16. When the healthy and hospitalized CD4+CD38+ percentage values were compared, a p-value of 0.44 was found. Finally, a comparison between the healthy and hospitalized CD8+CD38+ percentage values yielded a p-value of **0.006.

Discussion

Welch’s t-tests were used to analyze this data because the healthy and hospitalized sample sizes were not equal. A Welch’s t-test accounts for this difference in population size to calculate the significance of the data.

A 0.028 p-value was obtained when the healthy and hospitalized sample CD4+PD1+ percentage values were compared, indicating a significant difference between these two populations. PD-1 is a marker of T cell exhaustion, so a significant increase in PD-1 expression in CD4+ cells could indicate that these cells are dysregulated or in the process of becoming exhausted.

When the healthy and hospitalized CD8+CD38+ percentage values were compared, a p-value of 0.44 was found, indicating a highly significant change of CD38 expression during the progression of the COVID-19 infection. CD38 is a marker of T cell dysregulation as well, and a significant increase in CD38 expression in CD8 cells indicates a progression of dysregulation in these cells.

While the comparison between the rest of the healthy and hospitalized samples did not yield significant p-values, the surface molecule expressions follow the hypothesized pattern: PD-1 and CD38 expression increased, and CD127 expression decreased on average in the hospitalized samples compared to the healthy samples.

The majority of current data analyzing the phenotypes of T cells throughout COVID-19 infection have exclusively analyzed CD8+ T cells or centered the majority of their analyses around this subset of T cells. CD4+ T cells have great importance and impact on the immune system, and by analyzing the phenotypes of CD4+ T before and after COVID-19 infection, this project offers insight into how helper T cell function is affected by COVID-19.

References
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