How to Prevent yourself from Seeing Double

Abstract

The analysis of rare cells in flow cytometry data requires the acquisition of large data sets to visualize a sufficient number of events. Accurate identification of rare cells within a large number of events is hampered by the presence of artifacts and requires setting exclusionary gates to eliminate dying cells, cellular debris, autofluorescent cells, and cell doublets within analyzed populations (1). Doublet events are often observed in flow cytometry data, whether a sample is obtained from tissue or blood (2, 3). As cytometry data are further parsed to describe new rare populations of cells, the need for methods to properly discriminate contaminating doublets from true single cells has become an important aspect of data analysis. A newly described rare population of lymphocytes coexpressing both a T-cell receptor (TCR) and B-cell receptor (BCR), termed dual expressing (DE) cells (4) has been identified in the blood of patients with Type 1 diabetes (T1D). In this issue, Burel and colleagues (in this issue, page XXX) determine that commonly used analysis methods of nonimaging flow cytometric data do not efficiently resolve DE cells from a contaminating doublet population consisting of T-cell B-cell conjugates. The authors develop a gating strategy to limit T-and B-cell conjugate contamination from the analysis of DE cell populations through the use of imaging flow cytometry, which provides additional key parameters, such as bright-field area and bright-field aspect ratio. Utilizing this new strategy for doublet discrimination, the authors argue that a majority of DE cells, identified by traditional singlet gating strategies, are actually T-cell B-cell conjugates. Though they share a common stem cell progenitor, the development of the T- and B-cell lineages occur in spatially distinct locations, thymus, and bone marrow, respectively, via highly regulated and selective processes centered on the expression and signaling events that emanate from the T- or B-cell antigen receptors, TCR, and BCR (5, 6). Following development, naïve T cells circulate through the blood and secondary lymphoid organs surveilling an organism for the signs of foreign antigen (7). For major histocompatibility class II restricted CD4+ T cells, this surveillance occurs through interactions with antigen presenting cells (APCs) such as dendritic cells, monocytes, B cells, and macrophages (8). These interactions between T cells and APCs can result in the formation of cell: cell conjugates mediated through cell surface adhesion molecules (9). It is therefore not surprising that T-cell conjugates have been observed in the peripheral blood of humans. The frequency of T-cell conjugates and the phenotype of the cells that comprise them often change in response to an organisms' inflammatory state, such as following immunization or in cases of tuberculosis, dengue virus, and HIV infection (10, 11). These findings suggest that changes in T-cell conjugates are likely hallmarks of an active immune response. Two different types of gating strategies are commonly employed to exclude cell doublets from flow cytometry data. First, events can be excluded that deviate from the linear correlation between the forward scatter area (FSC-A) and the FSC height (FSC-H) parameters. Alternatively, events can be passed through two successive gates of FSC-A by FSC width (FSC-W) and side scatter area (SSC-A) by SSC width (SSC-W) utilizing the low pulse width signal indicative of single cells (Fig. 1) (3). While these methods are highly effective in eliminating the majority of contaminating doublets, not all T-cell conjugates are removed from the data by these criteria alone (3). To define cytometry analysis parameters that more efficiently discriminate T-cell conjugates from DE cells, Burel and colleagues leverage their knowledge of T-cell monocyte conjugates found in the peripheral blood of humans (10). Despite using a doublet discrimination gating strategy, they identify events expressing both T-cell and monocyte-specific markers CD3 and CD14. The authors find that mean fluorescent intensity (MFI) of both FCS-A and SSC-A increase between CD3+ CD14+ double-positive and single-positive populations. However, using high FSC-A and SSC-A intensity as exclusion criteria consequently removes single activated T cells as they are known to have increased size and granularity and thus have higher FCS-A and SSC-A values. When the levels of surface expressed lineage defining T cell and monocyte markers were investigated, it was determined that CD3+ CD14+ double positive population had comparable expression levels to single positive populations. One exception to this was the additive effect of the shared marker CD45, where the MFI substantially increased among the CD3+ CD14+ population relative to the single-positive populations, suggesting the majority of this population contained T-cell monocyte conjugates and not singlets. The authors also investigated the levels of transcripts in CD3+ CD14+ double-positive and single-positive populations using single-cell RNA sequencing (scRNAseq) analysis. They found a significant under representation of both monocyte and T-cell-enriched transcripts in the double-positive population compared to the single-positive populations. Furthermore, principle component analysis separates the double-positive population as an intermediate grouping between the clusters of T-cell and monocyte single-positive populations. These findings suggest strategies can be developed to filter out contaminating doublets in scRNAseq data sets generated from conventionally sorted populations. An effective strategy to identify CD3+ CD14+ single cells came through the use of imaging flow cytometry. By gating events with a high bright-field aspect ratio and low bright-field area, termed optimal (OPT) image gating (Fig. 1), data collected by imaging flow cytometry allowed efficient identification of single cells coexpressing the CD3 and CD14 markers and excluded T-cell monocyte conjugates. The frequency of these CD3+ CD14+ single cells identified by OPT image gating and imaging flow cytometry, however, was 50-fold reduced relative to the CD3+ CD14+ events observed using nonimaging flow cytometry and a traditional double exclusion gating strategy. The authors therefore argue that most CD3+ CD14+ events observed by nonimaging flow cytometry utilizing traditional double exclusion gating are often T-cell monocyte conjugates and not coexpressing single cells. The authors then applied their analysis protocol to the peripheral blood of healthy volunteers. Using nonimaging flow cytometric data and a traditional doublet discrimination gating strategy, they could identify the reported DE cells, defined as CD5+ CD19+ TCR+ cells (4). They observed that the DE population has a phenotype of higher MFIs for FSC-A, SSC-A, and CD45 than for CD5+ CD19− T cells, CD19+ CD5− B cells, or CD5+ CD19+ TCR− cells, consistent with observations for the CD3+ CD14+ population that contained mostly T-cell monocyte conjugates. The first report of TCR+ BCR+ DE cells came from cells isolated from the blood of people with T1D (4). B cells are thought to be critical APCs for the development of autoimmune diabetes in the nonobese diabetic animal models (12). Thus, in patients with T1D, islet-specific autoreactive T cells may form cell: cell conjugates with B cells. When imaging flow cytometry data were collected from human peripheral blood, the majority of DE cells identified through traditional doublet exclusion gating contained T- and B-cells conjugates, while with the OPT gating strategy most events were single cells that coexpressed CD19 and CD3. The studies by Burel and colleagues (in this issue, page XXX) describe a robust and broadly applicable doublet discrimination analysis through the use of imaging flow cytometry. Utilization of this method to enhance doublet exclusion in future studies will lead to a more accurate enumeration of DE expressing cells relative to T- and B-cell conjugates in people with Type 1 diabetes. Since T-cell conjugates are often observed in people with ongoing inflammatory immune responses (10, 11), changes in frequencies of T-cell conjugates and DE cells may provide a method to reveal the onset of autoimmune responses, as well as monitor the mobilization of immunity against infection in real time. Importantly, the accurate delineation of DE cells from T-cell conjugates will allow specific exploration of DE cell function and contribution to immune responses.

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