The present panel was optimized to quantify the relative frequencies of γδT-cells, invariant natural killer T-cells (iNKT-cells), and hematopoietic precursors in peripheral blood mononuclear cells (PBMC) from healthy individuals (Table 1). It works well with cryopreserved PBMC and we have observed similar results with fresh specimens. Other tissue types have not been tested. We developed this panel (Table 2) as part of a large study where we aim to survey the relative proportion of different immune cell subsets, including hematopoietic stem cells (HSC), in human peripheral blood specimens from healthy adults. It addresses HSC, γδT-cells, and iNKT-cells. HSC are multipotent precursor cells that give rise to all blood cell types, including the myeloid and lymphoid lineages. Though predominantly found in bone marrow and umbilical cord blood, they also occur at reduced frequencies in the blood 1, and can be identified by their expression of CD34 1, 2. In spite of being generally used as a molecular marker of HSCs, the function of CD34 is poorly understood 3. While most T-cells express a T-cell receptor (TCR) comprised of an α- and a β-chain, a minority of blood T-cells express the γδTCR. In healthy individuals, the vast majority of these have one of two phenotypes, representing ontologically separate lineages: DV1+ (previously Vδ1) cells are prevalent during fetal and early life, while DV2+ (previously Vδ2) cells usually dominate in adult blood 4, 5. The latter are usually GV9+ (previously Vγ9), but DV1 associates with a number of different Vγ chains 6. γδT-cells, in particular GV9/DV2 cells, are thought to act as a bridge between innate and acquired immunity 7. iNKT-cells express the AV24/BV11 TCR (previously Vα24/Vβ11) and recognize CD1d-restricted lipid antigens. The classical antigen used to detect these cells is the marine sponge-derived α-galactosylceramide (α-GalCer), though more common environmental Ags have recently been shown to also stimulate iNKT-cells 8, 9. CD1d molecules loaded with the α-GalCer analogue PBS-57 form more stable multimeric complexes than those loaded with α-GalCer, thus making a good tool to identify iNKT-cells 10. Three iNKT subsets have been characterized that differ in function, but also in CD4/CD8 expression: cytokine-producing CD4+ CD8− (predominant in fetal and neonatal blood), cytotoxic CD4− CD8−, and the rare IFN-γ-producing CD4− CD8+ iNKT-cells 11. Finally, we included Abs to CCR5, CCR7, CD27, CD28, and CD45RA in order to further explore the differentiation phenotypes of both γδT-cells and iNKT-cells (Figure 1). None to date. Additional and updated supporting information including technical details may be found in the online version of this article. Online Table 1 Instrument configuration. Online Table 2 Commercial reagents used in OMIP-019. Online Table 3 In-house conjugated reagents used in OMIP-019. Online Table 4 Priority rating for reagents. Online Table 5 Reagents tested but not included in final panel. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
This October, we celebrate the 10th anniversary of the Optimized Multicolor Immunofluorescence Panel (OMIP) publication format, a very successful manuscript type specifically designed for the flow cytometry and imaging communities. While the latter has not turned toward OMIPs, this publication type has more recently become adopted by the mass cytometry community. Back when flow cytometers were able to merely interrogate a handful of markers concomitantly, coming up with a workable panel was easy enough. This started changing as the number of measurable, and thus the number of measured, parameters gradually increased. As instrument capabilities ramped up to 18 fluorescent parameters, researchers found themselves spending what seemed like an inordinate amount of time developing good panels that would allow them to satisfactorily discern all markers and cellular subsets targeted in their study. Successful panels were being shared between labs but resulting study data were often published without proper acknowledgement of the person(s) that generated the panels, as the panels were viewed as just another laboratory tool. Although a thoughtfully designed and thoroughly optimized panel lies at the heart of any immunophenotyping study, the time and effort involved in generating a reagent panel that yields trustworthy and reproducible results were rarely reflected in the way that study results were being reported. Oftentimes, technical aspects of immunophenotyping methodologies were only incompletely described, and panel performance as well as gate placements was not always illustrated in sample plots. This and the fact that variations of a handful of panels (e.g., focusing on T-cell functionality) were generated independently in different labs again and again sparked the idea of developing a platform where highly optimized immunofluorescence panels could be published, with a focus on the technical aspects, the optimization processes, and very importantly on what was tested but did not work or did not work as well. This would provide a way to acknowledge the efforts of panel developers but also catalogue invaluable information that is typically hard to obtain, namely the technical nitty gritty, which is ultimately what can save others a lot of time and money when developing their own panels, in addition to being able to use existing panels as a backbone for their own. From this the OMIPs were born (1). Over the last 12 years or so, there has been a general drive in the flow cytometry community to make immunofluorescence assay reporting more transparent, with an ever-increasing number of journals now encouraging or requiring adherence to MIFlowCyt (2) and making fcs files available through the FlowRepository (3). The OMIPs fit right in with these initiatives, representing so much more than just ready-to-use panels by providing detailed discussions of panel design rationale, carefully delineating gating trees, and thoroughly outlining how an optimal panel performance was established. Two years after being established, OMIP submissions started coming in on a regular basis. Since then we have received a steady flow of OMIP candidate manuscripts, resulting in an average of seven protocols being published annually (Fig. 1). Since 2018, there has been a significant increase in OMIP publications. This is a testament to the continued relevance of such publications and underlines their increasing value with the advent of high-parameter flow cytometry. The OMIPs' sustained interest is apparent in their download rate, which is 2–3 times higher compared to other publications in Cytometry Part A. Many OMIPs are also highly cited, the top three being OMIP-044 (4), OMIP-34 (5), and OMIP-51 (6). Over the years, the level of panel complexity has been steadily increasing, reflecting the development and adoption of new technologies, such as mass cytometry, new dyes excitable in ranges of the light spectrum that were hitherto unused, and new affordable excitation light sources. In this issue, you will find the seven most recent OMIPs, with OMIP-069 being the first that is designed for full spectrum flow cytometry (this issue, pp. 1044). This panel interrogates 43 parameters by recording 40 fluorescent dyes, as well as three scatter signals, thus reaching the highest level of complexity within OMIPs to date. As exciting as the race for panels with ever increasing numbers of parameters is, it needs to be emphasized that the OMIPs' goal is to disseminate highly optimized panels that have been designed to address specific scientific questions. The increasing complexity of panels published as OMIPs observed over the years is a natural development that follows the increasing capability of flow cytometers and fluorophore availability during that time. Nevertheless, it is important to point out that panels comprised of less than 10 colors, such as OMIP-055 (7) can very well be eligible to become OMIPs if they appropriately address the stated biological question. We would like to also draw your attention to the special section in this issue on Genomic Cytometry, kindly guest edited by Robert Salomon, David Gallego-Ortega, and Christopher Hall. Combining OMIP-based high-content flow cytometry and cell sorting with downstream genomic single-cell scrutinization will help to further our understanding of biological mechanisms, and the flow community has been rapidly adopting this new technology.
A patient with refractory multiple myeloma received an infusion of CTL019 cells, a cellular therapy consisting of autologous T cells transduced with an anti-CD19 chimeric antigen receptor, after myeloablative chemotherapy (melphalan, 140 mg per square meter of body-surface area) and autologous stem-cell transplantation. Four years earlier, autologous transplantation with a higher melphalan dose (200 mg per square meter) had induced only a partial, transient response. Autologous transplantation followed by treatment with CTL019 cells led to a complete response with no evidence of progression and no measurable serum or urine monoclonal protein at the most recent evaluation, 12 months after treatment. This response was achieved despite the absence of CD19 expression in 99.95% of the patient's neoplastic plasma cells. (Funded by Novartis and others; ClinicalTrials.gov number, NCT02135406.).
8517 Background: CTL019, a 2nd-generation anti-CD19 CAR transduced via lentiviral vector, can induce regression of refractory B cell malignancies. Though multiple myeloma (MM) is reported to be CD19-negative, we hypothesized that CTL019 would exhibit efficacy in MM due to low-level CD19 expression on MM plasma cells (PC) or CD19 expression in drug-resistant, disease-propagating subsets of the MM clone. Here, we report initial results of an ongoing phase 1 study of CTL019 in patients with advanced MM. Methods: MM patients are eligible if they experienced disease progression within one year of a prior autologous stem cell transplantation (ASCT) and are medically fit to undergo second ASCT. Study therapy consists of 1-5x107CTL019 cells infused 12-14 days after high-dose melphalan + ASCT. Results: 4 subjects have been treated and have completed 30-220 days of follow-up. Median prior lines of therapy is 7.5 (range 3-10). 3/4 have unfavorable cytogenetics; 1/4 had PC leukemia. Adverse events have included hypogammaglobulinemia (4/4) and grade 1 cytokine release syndrome (1/4). 3 subjects are evaluable for response. In all 3 subjects, CTL019 engraftment was achieved (peak 0.1-0.6% of peripheral blood T cells at days 30-42), and B cells were not detectable by flow cytometry in blood or marrow at day 42. At day 100, subject #1 attained MRD-negative stringent complete response (CR), and Subject #2 attained MRD-negative unconfirmed (due to unevaluable bone marrow core) CR. Response duration in Subject #1 has surpassed the response duration after this subject's prior ASCT (i.e., remission inversion). 99.95% of Subject #1's MM PC were CD19-negative by flow cytometry and RTPCR, indicating that efficacy in this subject is not due to direct cytotoxicity of CTL019 against the dominant MM PC population. Subject #3 experienced disease progression at day 43. Updated results on the first 5 subjects will be presented. Conclusions: Preliminary data suggest that CTL019 can be manufactured from and safely administered to refractory MM patients. CTL019 can engraft and induce B cell aplasia after salvage ASCT. Ongoing, deep responses in 2 of 3 evaluable subjects are encouraging with respect to potential efficacy. Clinical trial information: NCT02135406.
This study shows that bone marrow (BM) stroma expresses constitutively multiple adhesion molecules (ICAM-1, VCAM-1, MadCAM-1, P-selectin) relevant for the homing and infiltration of BM by blood derived T lymphocytes, and also the co-stimulatory molecule CD80, relevant for T cell activation. T cells were capable of homing to BM but not to thymus. Homing to BM involved the integrins LFA-1alpha and alpha4 which interact with the above constitutively expressed cell adhesion molecules (CAMs). CD3 T cells were detected together with BM resident CD11c dendritic cells (DCs), often enriched in follicle-like structures in BM parenchyma. Cognate interactions between transferred antigen specific transgenic CD4 T cells and antigen laden BM-DCs led to formation of multicellular clusters in situ in BM, to generation of lymphoblasts and to clonal T cell expansion within such clusters. The great majority of BM-CD4 T cells had a memory phenotype suggesting that the BM microenvironment facilitates maintenance of CD4 memory. These results extend and corroborate our previous findings on BM-CD8 T cell mediated immune responses. Together these findings suggest that DC-T cell interactions in BM play an important role in immune responses to blood-borne antigen and in the establishment of systemic immunity and long-term memory.
The bone marrow has been shown to represent a unique microenvironment where T cell immune responses against tumor associated antigens can be initiated and tumor-immune memory T cells are enriched. In the present study the graft versus leukemia (GvL) reactivity of bone marrow derived tumor-immune memory T cells was analyzed in an allogeneic minor histocompatibility different murine GvL tumor model with late stage disease. A single adoptive cell transfer of B10.D2 donor immune bone marrow cells (iBM) into sub-lethally irradiated late stage ESb-MP tumor-bearing DBA/2 mice led to a complete tumor remission and to significant life prolongation. Even though the frequency of bone marrow resident T cells is only around 2%, the GvL reactivity of iBM was superior to immune cells from the spleen (iSPL) or to those from the peritoneal cavity (iPEC) of the same immunized donors. iPEC exerted mostly unwanted graft versus host (GvH) reactivity, while iSPL exerted GvL and GvH reactivity. Bone marrow cells from naive donor mice or T cell depleted iBM cells were completely devoid of GvL reactivity. The low number of tumor-immune memory T cells thus conferred the GvL reactivity of iBM cells.
The present panel was optimized for the evaluation of CD4+ and CD8+ T-cell responses to various HIV-1–derived peptide pools in peripheral blood mononuclear cells (PBMC) from HIV-1+ individuals with differences in clinical progression. It works well with cryopreserved PBMC, and we have observed similar results with fresh specimens. Other tissue types have not been tested., 1 Example staining and gating. A: Identification of T-cell subsets. After selecting live CD3+CD14−CD19− single cells, eventual dye aggregates are excluded (gray box) and a lymphocyte gate set. CD4+ and CD8+ T-cells are then selected for further analysis. B: Selection of cytokine-producing cells after gating as shown in (A). CD4+ and CD8+ T-cells positive for either IFN-γ, TNF-α, or IL-2 are separately gated. Besides analyzing the cytokine pattern (combination of cytokines produced on a per cell basis) produced in response to antigenic stimulation, a Boolean gate encompassing all cytokine positive cells (cyt+) is created to evaluate the total Ag-specific response. C: Phenotypic analysis of Ag-specific cells gated as described in (B). Total CD4+ and CD8+ T-cells (in gray) are used as a reference when analyzing the cell surface phenotype of cyt+ cells (in red). Shown are cryopreserved PBMC from an HIV-1+ subject stimulated with an HIV Gag peptide pool. The approach used for the development of this panel has been described in detail (1). Briefly, a large number of Ab-conjugates were screened for each antigen of interest, as available, to select those Ab-conjugates providing best detection. As the focus of the panel was the detection of cytokine-producing T-cells, the brightest fluorochromes were used for interleukin-2 (IL-2), interferon (IFN)-γ, and tumor necrosis factor (TNF)-α. Next, priority was given to PD-1 and CCR7, as these antigens are expressed at low molecular densities. After selecting a dump channel to exclude dead cells, B-cells and monocytes/macrophages from the analysis, a range of Ab-conjugates for other markers used for T-cell subset definition and determination of activation status were tested in the free detectors until optimal detection of all antigens was achieved. To this end, CD4-QD655, which was included in early panels, was replaced with CD4-QD605 to improve the detection threshold of CD28-PE-Cy5 on CD4+ T-cells. PD-1, which is labeled with QD655 in the final panel, does not influence the detection threshold of PD-1+ CD28+ cells in the same way. This is because PD-1 has a lower expression level (and thus a lower measured mean fluorescence intensity) than CD4, thereby causing less spillover into other detectors. CCR7 was labeled at 37°C (2), while CD3 was labeled after fixing and permeabilizing the cells, so as not to inadvertently exclude any relevant cells that might have internalized their T-cell receptor/CD3 complexes after activation (3). Fluorescently conjugated CD28 Ab was added to the stimulation cultures, thus serving as a costimulator during the cultures while at the same time, labeling CD28 molecules. The total number of cells acquired determines the detection sensitivity of cytokine-producing cells. Thus, to reliably quantify cytokine responses, higher number of cells should be acquired as the frequency of responding cells decreases. None to date. Technical details may be found in Supporting Information in the online version of this article. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
