Analysis Of Flow Cytometry Data Research Articles

EasyFlow: An open-source, user-friendly cytometry analyzer with graphic user interface (GUI).

Flow cytometry enables quantitative measurements of fluorescence in single cells. The technique was widely used for immunology to identify populations with different surface protein markers. More recently, the usage of flow cytometry has been extended to additional readouts, including intracellular proteins and fluorescent protein transgenes, and is widely utilized to study developmental biology, systems biology, microbiology, and many other fields. A common file format (FCS format, defined by the International Society for Advancement of Cytometry (ISAC)) has been universally adopted, facilitating data exchange between different machines. A diverse spectrum of software packages has been developed for the analysis of flow cytometry data. However, those are either 1) costly proprietary softwares, 2) open source packages with prerequisite installation of R or Python and sometimes require users to have experience in coding, or 3) online tools that are limiting for analysis of large data sets. Here, we present EasyFlow, an open-source flow cytometry analysis graphic user interface (GUI) based on Matlab or Python, that can be installed and run locally across platforms (Windows, MacOS, and Linux) without requiring previous coding knowledge. The Python version (EasyFlowQ) is also developed on a popular plotting framework (Matplotlib) and modern user interface toolkit (Qt), allowing more advanced users to customize and keep contributing to the software, as well as its tutorials. Overall, EasyFlow serves as a simple-to-use tool for inexperienced users with little coding experience to use locally, as well as a platform for advanced users to further customize for their own needs.

Automated analysis of flow cytometry data with minimal training files: Research evaluation of an elastic image registration algorithm for TBNK, stem cell enumeration, and lymphoid screening tube assays.

Automated analysis of flow cytometry data can improve objectivity and reduce analysis time but has generally required work by software and algorithm experts. Here, we investigated the performance of BD ElastiGate™ Software (hereafter ElastiGate), which allows users to automate gating by selecting gated training files, then uses elastic image registration to gate new files. Three assays of increasing complexity were examined: TBNK, stem cell enumeration (SCE), and lymphoid screening tube (LST). For TBNK analysis, 60 peripheral blood (PB) samples from normal, HIV+, and controls were tested with ground truth analysis by an existing automated method. For SCE, 128 samples including bone marrow (BM), cord blood (CB), and apheresis were tested with analysis by multiple manual analysts. For LST, 80 PB and 28 BM samples were tested with manual analysis. For ElastiGate, a minimal number of training files was selected. Results were compared by Bland-Altman or F1 score analysis. For TBNK, ElastiGate using three training files (1 control, 1 normal, 1 HIV+) showed mean %bias across all reported populations between -1.48% and 7.13% (average 2.08%). For SCE, ElastiGate using three BM and two CB training files showed median F1 scores >0.93 in comparison to >0.94 and >0.92 for two other manual analysts. For LST, ElastiGate using four training files for each of PB and BM showed median F1 scores >0.945 for 13 of 14 PB populations and 10 of 14 BM populations, with generally similar or better performance for normal samples compared to abnormal; populations with lower scores were often associated with lower agreement between manual analysts. Based on analysis of three assays with four sample types of increasing complexity, ElastiGate with minimal training files may perform as an automated gating assistant. The results reported here are for research use only, not for use in diagnostic or therapeutic procedures.

Subclonal Populations Detected by Flow Cytometry in Chronic Lymphocytic Leukemia

Abstract Introduction Chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) is a mature, small B-cell lymphoma that typically co-expresses CD5 and CD23. Molecular-targeted therapies have produced remarkable therapeutic benefits in CLL, yet many patients are resistant to treatment. This study aims to identify subpopulations in CLL by conventional flow cytometry and clustering analysis. Methods One-hundred-and-forty-one CLL cases were taken from a public database and analyzed by uniform manifold approximation and projection (UMAP). Differences in size, granularity, and intensities of CD19, CD20, CD79a, CD5, CD23, CD10, and FMC7 were plotted with BD FACSDiva Software. Eighteen cases with a new diagnosis of CLL at our institution between January 1, 2021 to December 31, 2022 were analyzed. Only peripheral blood samples were included. Markers including forward scatter, side scatter, and fluorescent intensities of CD19, CD20, CD5, CD23, CD10, surface kappa, and surface lambda were used to identify discrete CLL clusters in UMAP plots. Results: Public Dataset Clustering analysis of conventional flow cytometry data from 141 cases from a public database reveals discrete populations of cells exhibiting varying sizes, granularity and fluorescent intensities. Among the 141 cases, 33 (23.4%) had 1 cluster, 64 (45.4%) with 2 clusters, 42 (29.8%) with 3 clusters and 2 (1.4%) with 4 clusters. Among the cases with 2 clusters, a smaller population of cells deviated from the main neoplastic population. This smaller population had a higher forward and side scatter, and brighter intensities in CD45 and CD19, consistent with prolymphocytes. Among the cases with 3 clusters, a third population recurrently had a smaller size by forward scatter. A few cases additionally had varying intensities by CD23 and CD19. Among the 2 cases with 4 clusters, only slight deviations by UMAP were seen and may be artifactual. Institutional Dataset Clustering analysis of 18 cases at our institution revealed complex patterns of cell clusters exhibiting varying sizes, granularity and surface receptor expression intensity. Among the 18 cases, 1 (5.6%) had 1 cluster, 1 (5.6%) with 2 clusters, 10 (55.6%) with 3 clusters, 3 (16.7%) with 4 clusters, and 3 (16.7%) with 5 clusters. Similar to the public dataset, the third population, in addition to the predominant population and prolymphocytes, consistently varied by lower forward scatter. Additional clusters, such as a population with varying CD23 signal intensities, were identified in some cases. Conclusions In our study, multiple clonal subsets in CLL/SLL are detected by conventional flow cytometry analysis. These subclonal populations may result in therapy resistance for these patients, and identifying them by flow cytometry analysis may provide additional prognostic information in the clinical setting. Furthermore, the ability to detect these subclonal populations may allow for isolation of specific clones for future investigation in research settings.

Deep Learning-Based Integration of Morphology and Immunophenotype Predicts Molecular Genetic Subtypes of Multiple Myeloma

Abstract Multiple myeloma, the second most common hematologic malignancy, is characterized by recurrent molecular genetic variants which portend important prognostic and therapeutic implications. These variants, which include chromosomal aneuploidies, translocations, and other structural changes, are currently identified by performing karyotyping and fluorescence in situ hybridization analysis on bone marrow biopsy samples. Comprehensive variant testing for full characterization of multiple myeloma cases can take days to weeks, is costly, and is not available to all patients. Past studies have explored to what extent cellular morphologic information from bone marrow aspirates or immunophenotypic information from flow cytometry can predict these genetic variants via the use of machine learning algorithms. However, the integration of multiple hematopathology data modalities using machine learning towards improving the diagnosis and characterization of hematologic neoplasms has yet to be explored. To this end, we developed a deep learning-based algorithm which, for individual patients, integrates tens-of-thousands of cell images from Wright-stained bone marrow aspirate smears with hundreds-of-thousands of events from myeloma flow cytometry panels to predict the presence or absence of particular molecular genetic variants among 85 cases of multiple myeloma. Attention-based multi-instance learning models were developed utilizing convolutional neural networks to analyze cell image data and multi-layer perceptrons to analyze flow cytometry data. Models trained solely on cell image data showed variable performance in predicting genetic subtypes of multiple myeloma cases: t(11;14), AUROC = 0.795; 1q+, AUROC = 0.743; del(17p), AUROC = 0.520. Similar models trained solely on flow cytometry data showed fair performance for all subtypes: t(11;14), AUROC = 0.761; 1q+, AUROC = 0.761; del(17p), AUROC = 0.787. However, models which combine features among high-attention events from both cell image and flow cytometry data yield significantly improved performance: t(11;14), AUROC = 0.892; 1q+, AUROC = 0.789; del(17p), AUROC = 0.876. Similarly, models trained to predict established prognostic classifications of multiple myeloma cases performed best when cell image and flow cytometry data is integrated: cell images alone, AUROC = 0.721; flow cytometry alone, AUROC = 0.827; both data combined, AUROC = 0.839. Additional improvements in prognostic classification were obtained when utilizing a Kronecker product to more completely utilize pairwise interactions between cell image and flow cytometry features: AUROC = 0.864. This study is, to our knowledge, the first to utilize deep learning to integrate multiple hematopathology data modalities towards automated diagnosis or subclassification of hematologic neoplasms in individual patients. The results of this study demonstrate the feasibility of accurately predicting molecular genetic subtypes of multiple myeloma cases solely from cell morphology and flow cytometric information. With further refinement, these promising models could be applied in low resource settings as well as research settings to better understand the biological relationship between plasma cell genetics with morphology and immunophenotype.

AutoGater: a weakly supervised neural network model to gate cells in flow cytometric analyses

Flow cytometry is a useful and efficient method for the rapid characterization of a cell population based on the optical and fluorescence properties of individual cells. Ideally, the cell population would consist of only healthy viable cells as dead cells can confound the analysis. Thus, separating out healthy cells from dying and dead cells, and any potential debris, is an important first step in analysis of flow cytometry data. While gating of debris can be conducted using measured optical properties, identifying dead and dying cells often requires utilizing fluorescent stains (e.g. Sytox, a nucleic acid stain that stains cells with compromised cell membranes) to identify cells that should be excluded from downstream analyses. These stains prolong the experimental preparation process and use a flow cytometer’s fluorescence channels that could otherwise be used to measure additional fluorescent markers within the cells (e.g. reporter proteins). Here we outline a stain-free method for identifying viable cells for downstream processing by gating cells that are dying or dead. AutoGater is a weakly supervised deep learning model that can separate healthy populations from unhealthy and dead populations using only light-scatter channels. In addition, AutoGater harmonizes different measurements of dead cells such as Sytox and CFUs.

Terminal α1,2-fucosylation of glycosphingolipids by FUT1 is a key regulator in early cell-fate decisions

The embryonic cell surface is rich in glycosphingolipids (GSLs), which change during differentiation. The reasons for GSL subgroup variation during early embryogenesis remain elusive. By combining genomic approaches, flow cytometry, confocal imaging, and transcriptomic data analysis, we discovered that α1,2-fucosylated GSLs control the differentiation of human pluripotent cells (hPCs) into germ layer tissues. Overexpression of α1,2-fucosylated GSLs disrupts hPC differentiation into mesodermal lineage and reduces differentiation into cardiomyocytes. Conversely, reducing α1,2-fucosylated groups promotes hPC differentiation and mesoderm commitment in response to external signals. We find that bone morphogenetic protein 4 (BMP4), a mesodermal gene inducer, suppresses α1,2-fucosylated GSL expression. Overexpression of α1,2-fucosylated GSLs impairs SMAD activation despite BMP4 presence, suggesting α-fucosyl end groups as BMP pathway regulators. Additionally, the absence of α1,2-fucosylated GSLs in early/late mesoderm and primitive streak stages in mouse embryos aligns with the hPC results. Thus, α1,2-fucosylated GSLs may regulate early cell-fate decisions and embryo development by modulating cell signaling.

Quantification of the median fluorescence intensity of CD3 and CD4 in mycosis fungoides/Sezary syndrome versus non-neoplastic control cases in peripheral blood.

Peripheral blood involvement by MF/SS has significant implications for prognosis and treatment. Flow cytometry is commonly used to assess MF/SS by analyzing the ratio of CD26- and/or CD7-CD4 + T cells and assessment of immunophenotypic abnormalities. However, distinguishing normal from abnormal cells is not always easy. In this study, we aimed to establish quantitative thresholds to better distinguish normal CD4 + T cells from neoplastic CD4 + T cells. A retrospective analysis of flow cytometry data was performed on 30 MF/SS patients with a detectable abnormal T cell population (positive), 63 patients with suspected or confirmed cutaneous involvement without a detectable abnormal T cell population (negative), and 60 healthy controls (control). CD3 and CD4 median fluorescence intensity (MFI) was normalized to internal control subsets. Among the positive cases, 50% had CD3 expression outside ± 2 SD from the mean of the negative and control group in the CD4 + CD26- subset. The corresponding specificity of this threshold was 94%. The ± 2 SD threshold showed a sensitivity of 57% and a specificity of 94% for the CD3 intensity among the CD7-negative subset. For CD4 intensity, the ± 2 SD threshold had a sensitivity of 33.3% and specificity of 95% for the CD26-negative subset and a sensitivity of 37% and specificity of 95% for the CD7-negative subset. In our study, although changes in CD3 and CD4 intensity greater than ± 2 SD were specific for MF/SS, more subtle differences in the intensity of CD3 and CD4 should not be used as the sole abnormality to make a diagnosis of circulating MF/SS.

Blood immune cell profiling in adults with longstanding type 1 diabetes is associated with macrovascular complications.

There is increasing evidence for heterogeneity in type 1 diabetes mellitus (T1D): not only the age of onset and disease progression rate differ, but also the risk of complications varies markedly. Consequently, the presence of different disease endotypes has been suggested. Impaired T and B cell responses have been established in newly diagnosed diabetes patients. We hypothesized that deciphering the immune cell profile in peripheral blood of adults with longstanding T1D may help to understand disease heterogeneity. Adult patients with longstanding T1D and healthy controls (HC) were recruited, and their blood immune cell profile was determined using multicolour flow cytometry followed by a machine-learning based elastic-net (EN) classification model. Hierarchical clustering was performed to identify patient-specific immune cell profiles. Results were compared to those obtained in matched healthy control subjects. Hierarchical clustering analysis of flow cytometry data revealed three immune cell composition-based distinct subgroups of individuals: HCs, T1D-group-A and T1D-group-B. In general, T1D patients, as compared to healthy controls, showed a more active immune profile as demonstrated by a higher percentage and absolute number of neutrophils, monocytes, total B cells and activated CD4+CD25+ T cells, while the abundance of regulatory T cells (Treg) was reduced. Patients belonging to T1D-group-A, as compared to T1D-group-B, revealed a more proinflammatory phenotype characterized by a lower percentage of FOXP3+ Treg, higher proportions of CCR4 expressing CD4 and CD8 T cell subsets, monocyte subsets, a lower Treg/conventional Tcell (Tconv) ratio, an increased proinflammatory cytokine (TNFα, IFNγ) and a decreased anti-inflammatory (IL-10) producing potential. Clinically, patients in T1D-group-A had more frequent diabetes-related macrovascular complications. Machine-learning based classification of multiparameter flow cytometry data revealed two distinct immunological profiles in adults with longstanding type 1 diabetes; T1D-group-A and T1D-group-B. T1D-group-A is characterized by a stronger pro-inflammatory profile and is associated with a higher rate of diabetes-related (macro)vascular complications.

Integrated Single-Cell RNA-seq and ATAC-seq Reveals Heterogeneous Differentiation of CD4+ Naive T Cell Subsets is Associated with Response to Antidepressant Treatment in Major Depressive Disorder.

The mechanism involved in major depressive disorder (MDD) is well-studied but the mechanistic origin of the heterogeneous antidepressant effect remains largely unknown. Single-cell RNA-sequencing (scRNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) on peripheral blood mononuclear cells from 8 healthy individuals and 8 MDD patients before or after 12 weeks of antidepressant treatment is performed. scRNA-seq analysis reveals a lower proportion of naive T cells, particularly CD4+ naive T cells, in MDD patients compared to controls, and in nonresponders versus responders at the baseline. Flow cytometry data analysis of an independent cohort of 35 patients and 40 healthy individuals confirms the findings. Enrichment analysis of differentially expressed genes indicated obvious immune activation in responders. A specific activated CD4+ naive T population in responders characterized by enhanced mitogen-activated protein kinases (MAPK)pathway is identified. E-twenty six (ETS) is proposed as an upstream regulator of the MAPK pathway and heterogeneous differentiation in activated CD4+ naive T population is associated with the response to antidepressant treatment in MDD patients. A distinct immune feature manifested by CD4+ naive T cells during antidepressant treatment in MDD is identified. Collectively, this proposes the molecular mechanism that underlies the heterogeneous antidepressant outcomes for MDD.

Unbiased method for spectral analysis of cells with great diversity of autofluorescence spectra.

Autofluorescence is an intrinsic feature of cells, caused by the natural emission of light by photo-excitatory molecular content, which can complicate analysis of flow cytometry data. Different cell types have different autofluorescence spectra and, even within one cell type, heterogeneity of autofluorescence spectra can be present, for example, as a consequence of activation status or metabolic changes. By using full spectrum flow cytometry, the emission spectrum of a fluorochrome is captured by a set of photo detectors across a range of wavelengths, creating an unique signature for that fluorochrome. This signature is then used to identify, or unmix, that fluorochrome's unique spectrum from a multicolor sample containing different fluorescent molecules. Importantly, this means that this technology can also be used to identify intrinsic autofluorescence signal of an unstained sample, which can be used for unmixing purposes and to separate the autofluorescence signal from the fluorophore signals. However, this only works if the sample has a singular, relatively homogeneous and bright autofluorescence spectrum. To analyze samples with heterogeneous autofluorescence spectral profiles, we setup an unbiased workflow to more quickly identify differing autofluorescence spectra present in a sample to include as "autofluorescence signatures" during the unmixing of the full stained samples. First, clusters of cells with similar autofluorescence spectra are identified by unbiased dimensional reduction and clustering of unstained cells. Then, unique autofluorescence clusters are determined and are used to improve the unmixing accuracy of the full stained sample. Independent of the intensity of the autofluorescence and immunophenotyping of cell subsets, this unbiased method allows for the identification of most of the distinct autofluorescence spectra present in a sample, leading to less confounding autofluorescence spillover and spread into extrinsic phenotyping markers. Furthermore, this method is equally useful for spectral analysis of different biological samples, including tissue cell suspensions, peripheral blood mononuclear cells, and in vitro cultures of (primary) cells.

SWIFT clustering analysis of intracellular cytokine staining flow cytometry data of the HVTN 105 vaccine trial reveals high frequencies of HIV-specific CD4+ T cell responses and associations with humoral responses.

The HVTN 105 vaccine clinical trial tested four combinations of two immunogens - the DNA vaccine DNA-HIV-PT123, and the protein vaccine AIDSVAX B/E. All combinations induced substantial antibody and CD4+ T cell responses in many participants. We have now re-examined the intracellular cytokine staining flow cytometry data using the high-resolution SWIFT clustering algorithm, which is very effective for enumerating rare populations such as antigen-responsive T cells, and also determined correlations between the antibody and T cell responses. Flow cytometry samples across all the analysis batches were registered using the swiftReg registration tool, which reduces batch variation without compromising biological variation. Registered data were clustered using the SWIFT algorithm, and cluster template competition was used to identify clusters of antigen-responsive T cells and to separate these from constitutive cytokine producing cell clusters. Registration strongly reduced batch variation among batches analyzed across several months. This in-depth clustering analysis identified a greater proportion of responders than the original analysis. A subset of antigen-responsive clusters producing IL-21 was identified. The cytokine patterns in each vaccine group were related to the type of vaccine - protein antigens tended to induce more cells producing IL-2 but not IFN-γ, whereas DNA vaccines tended to induce more IL-2+ IFN-γ+ CD4 T cells. Several significant correlations were identified between specific antibody responses and antigen-responsive T cell clusters. The best correlations were not necessarily observed with the strongest antibody or T cell responses. In the complex HVTN105 dataset, alternative analysis methods increased sensitivity of the detection of antigen-specific T cells; increased the number of identified vaccine responders; identified a small IL-21-producing T cell population; and demonstrated significant correlations between specific T cell populations and serum antibody responses. Multiple analysis strategies may be valuable for extracting the most information from large, complex studies.

#2084 Biologic effect evaluation of de novo long-term release tacrolimus on metabolizer phenotype and T cell composition after kidney transplantation

Abstract Background and Aims Kidney transplantation (KTX) is the current treatment of choice in patients with kidney failure. Since immunosuppressive medication to prevent acute rejection is still needed in high doses with frequent adverse events, new strategies to achieve risk-appropriate personalization of immunosuppression are needed. Calcineurin inhibitors are the cornerstone of T cell suppression, currently available in different release formulations including once daily long-term release tacrolimus (LCPT) and regular twice daily immediate release tacrolimus (IRT). Little is known about the temporal evolution of tacrolimus metabolism potentially influencing T cell composition and clinical endpoints between different tacrolimus formulations after KTX. Method To evaluate the influence of de novo LCPT on clinical endpoints, metabolizer phenotype and immune cell composition after KTX, subgroup analysis of a single-center, prospective, observational cohort study including 32 patients started on LCPT (0.1 mg/kg) and 55 patients started on IRT (0.1 mg/kg) was conducted. Peripheral blood mononuclear cells were isolated from whole blood samples drawn before KTX, 2 months and 12 months after KTX for flow cytometry analysis. Study visits were simultaneously conducted to obtain tacrolimus dosing, plasma levels, acute rejection and infection incidence. Statistical analysis was done using GraphPad Prism (v10), accepting significance for p < 0.05, and flow cytometry analysis was done using FlowJo (v10) software. Results The cohort represented KTX recipients with a median age of 60 years and similar distributions of gender, HLA-mismatches and induction therapy. Clinical outcome data showed a trend towards lower acute rejection rate, lower serum-creatinine values and increased incidence of BK-viremia in LCPT treated patients. Rejection rate was associated with a fast-metabolizer phenotype (HR 3.9; 1.1-6.8, p < 0.01) and HLA mismatch > 3 (HR 4.0; 1.2-7.3, p < 0.01) while this effect was blunted in the LCPT group (HR 1.9; 0.7-3.2, p = 0.67 and HR 0.9; 0.3-1.4, p = 0.38 respectively) in a multivariate Cox-regression model. Cluster exploration and tSNE analysis of flow cytometry data revealed diminution of activated-proliferative effector and regulatory T cell trajectories, with significant reduction in cell counts of CD4+CD15s+/CD45RA-Ki67+Foxp3+ Treg and in CD4+CD147high/CD45RA-LAP+HLADR+FcRL3+ effector T cells in LCPT treated patients after KTX. Conclusion Induction with de novo LCPT was associated with a blunted influence of metabolizer phenotype and HLA-mismatch on acute rejection in our cohort. Major changes were detected in peripheral T cell composition after transplantation, with signs of a stronger immunosuppressive effect of LCPT on circulating activated T cells. Therefore, LCPT may be considered for KTX recipients with higher HLA-mismatch (i.e. “old-for-old”) or fast-metabolizer phenotype. Inclusion of changes in T cell composition as biologic effect measures may be worthwhile in future studies evaluating biomarkers to guide immunosuppression and predict complications after KTX.

Analysis of flow cytometry data from ultrasound-guided lymph node biopsies with two types of needles.

In this study, we combined two techniques, ultrasound-guided needle biopsy and flow cytometry (FCM), to explore their value in patients with enlarged lymph nodes. We compared the results of 198 needle biopsies on FCM and pathology. Forty-two were done by (fine needle aspiration, FNA), and the remaining 156 with (core needle biopsy, CNB), in 36 of 156 patients, a FNA was performed in the same lymph node after completion of the CNB. Except for five types of pathological entities, the rest were differentiated only detected or undetected tumours as the outcome distinction. Among the 198 needle biopsies, 13 were inadequate specimens, while the remaining 185 had pathological findings, including 47 benign and 138 neoplastic findings. Thirty-six patients underwent puncture with both FNA and CNB, both needles produced identical results by FCM, but more cells were obtained by FNA. Among the pathologically positive results, there were 23 missed diagnoses in FCM, in contrast, evidence of tumours was observed in the FCM images of 15 needle biopsies that reported benign or findings that were inconsistent with pathology, and the final diagnosis was consistent with the FCM in 10 cases. FCM detected haematolymphoid tumours with a sensitivity of 87.8% and a specificity of 91.9%. The combination of FCM and ultrasound-guided lymph node needle biopsy can quickly provide guidance for clinical decision-making. We recommend that all lymph node needle biopsies be sent for FCM, the specimen can be obtained by the last puncture with FNA.

Distinct responses to warming within picoplankton communities across an environmental gradient.

Picophytoplankton are a ubiquitous component of marine plankton communities and are expected to be favored by global increases in seawater temperature and stratification associated with climate change. Eukaryotic and prokaryotic picophytoplankton have distinct ecology, and global models predict that the two groups will respond differently to future climate scenarios. At a nearshore observatory on the Northeast US Shelf, however, decades of year-round monitoring have shown these two groups to be highly synchronized in their responses to environmental variability. To reconcile the differences between regional and global predictions for picophytoplankton dynamics, we here investigate the picophytoplankton community across the continental shelf gradient from the nearshore observatory to the continental slope. We analyze flow cytometry data from 22 research cruises, comparing the response of picoeukaryote and Synechococcus communities to environmental variability across time and space. We find that the mechanisms controlling picophytoplankton abundance differ across taxa, season, and distance from shore. Like the prokaryote, Synechococcus, picoeukaryote division rates are limited nearshore by low temperatures in winter and spring, and higher temperatures offshore lead to an earlier spring bloom. Unlike Synechococcus, picoeukaryote concentration in summer decreases dramatically in offshore surface waters and exhibits deeper subsurface maxima. The offshore picoeukaryote community appears to be nutrient limited in the summer and subject to much greater loss rates than Synechococcus. This work both produces and demonstrates the necessity of taxon- and site-specific knowledge for accurately predicting the responses of picophytoplankton to ongoing environmental change.

Abstract LB338: Targeted deletion of CD244 on monocytes promotes differentiation into anti-tumorigenic macrophages and potentiates PD-L1 blockade in melanoma

Abstract Background: In the myeloid compartment of the tumor microenvironment, CD244 signaling has been implicated in immunosuppressive phenotype of monocytes. However, the precise molecular mechanism and contribution of CD244 to tumor immunity in monocytes/macrophages remains elusive due to the co-existing lymphoid cells expressing CD244. Methods: To directly assess the role of CD244 in tumor-associated macrophages, monocyte-lineage-specific CD244-deficient mice were generated using cre-lox recombination and challenged with B16F10 melanoma. The phenotype and function of tumor-infiltrating macrophages along with antigen-specific CD8 T cells were analyzed by flow cytometry and single cell RNA sequencing data analysis, and the molecular mechanism underlying anti-tumorigenic macrophage differentiation, antigen presentation, phagocytosis was investigated ex vivo. Finally, the clinical feasibility of CD244-negative monocytes as a therapeutic modality in melanoma was confirmed by adoptive transfer experiments. Results: CD244fl/flLysMcre mice demonstrated a significant reduction in tumor volume (61% relative to that of the CD244fl/fl control group) 14 days after tumor implantation. Within tumor mass, CD244fl/flLysMcre mice also showed higher percentages of Ly6Clow macrophages, along with elevated gp100+IFN-γ+ CD8 T cells. Flow cytometry and RNA sequencing data demonstrated that ER stress resulted in increased CD244 expression on monocytes. This, in turn, impeded the generation of anti-tumorigenic Ly6Clow macrophages, phagocytosis and MHC-I antigen presentation by suppressing autophagy pathways. Combining anti-PD-L1 antibody with CD244-/- bone marrow-derived macrophages markedly improved tumor rejection compared to the anti-PD-L1 antibody alone or in combination with wild-type macrophages. Consistent with the murine data, transcriptome analysis of human melanoma tissue single-cell RNA-sequencing dataset revealed close association between CD244 and the inhibition of macrophage maturation and function. Furthermore, the presence of CD244-negative monocytes/macrophages significantly increased patient survival in primary and metastatic tumors. Conclusion: Our study highlights the novel role of CD244 on monocytes/macrophages in restraining anti-tumorigenic macrophage generation and tumor antigen-specific T cell response in melanoma. Importantly, our findings suggest that CD244-deficient macrophages could potentially be used as a therapeutic agent in combination with immune checkpoint inhibitors. Furthermore, CD244 expression in monocyte-lineage cells serve as a prognostic marker in cancer patients. Citation Format: Jeongsoo Kim, Tae-Jin Kim, Sehyun Chae, Hyojeong Ha, Yejin Park, Seon Ah Lim, Hyemin Lee, Younji Sim, Seyoung Oh, Geon Mo Koo, Minkyung Jo, Daehee Hwang, Kyung-Mi Lee. Targeted deletion of CD244 on monocytes promotes differentiation into anti-tumorigenic macrophages and potentiates PD-L1 blockade in melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB338.

Targeted deletion of CD244 on monocytes promotes differentiation into anti-tumorigenic macrophages and potentiates PD-L1 blockade in melanoma

BackgroundIn the myeloid compartment of the tumor microenvironment, CD244 signaling has been implicated in immunosuppressive phenotype of monocytes. However, the precise molecular mechanism and contribution of CD244 to tumor immunity in monocytes/macrophages remains elusive due to the co-existing lymphoid cells expressing CD244.MethodsTo directly assess the role of CD244 in tumor-associated macrophages, monocyte-lineage-specific CD244-deficient mice were generated using cre-lox recombination and challenged with B16F10 melanoma. The phenotype and function of tumor-infiltrating macrophages along with antigen-specific CD8 T cells were analyzed by flow cytometry and single cell RNA sequencing data analysis, and the molecular mechanism underlying anti-tumorigenic macrophage differentiation, antigen presentation, phagocytosis was investigated ex vivo. Finally, the clinical feasibility of CD244-negative monocytes as a therapeutic modality in melanoma was confirmed by adoptive transfer experiments.ResultsCD244fl/flLysMcre mice demonstrated a significant reduction in tumor volume (61% relative to that of the CD244fl/fl control group) 14 days after tumor implantation. Within tumor mass, CD244fl/flLysMcre mice also showed higher percentages of Ly6Clow macrophages, along with elevated gp100+IFN-γ+ CD8 T cells. Flow cytometry and RNA sequencing data demonstrated that ER stress resulted in increased CD244 expression on monocytes. This, in turn, impeded the generation of anti-tumorigenic Ly6Clow macrophages, phagocytosis and MHC-I antigen presentation by suppressing autophagy pathways. Combining anti-PD-L1 antibody with CD244−/− bone marrow-derived macrophages markedly improved tumor rejection compared to the anti-PD-L1 antibody alone or in combination with wild-type macrophages. Consistent with the murine data, transcriptome analysis of human melanoma tissue single-cell RNA-sequencing dataset revealed close association between CD244 and the inhibition of macrophage maturation and function. Furthermore, the presence of CD244-negative monocytes/macrophages significantly increased patient survival in primary and metastatic tumors.ConclusionOur study highlights the novel role of CD244 on monocytes/macrophages in restraining anti-tumorigenic macrophage generation and tumor antigen-specific T cell response in melanoma. Importantly, our findings suggest that CD244-deficient macrophages could potentially be used as a therapeutic agent in combination with immune checkpoint inhibitors. Furthermore, CD244 expression in monocyte-lineage cells serve as a prognostic marker in cancer patients.

Persistence of KIRneg NK cells after haploidentical hematopoietic stem cell transplantation protects from human cytomegalovirus infection/reactivation.

Haploidentical hematopoietic stem cell transplantation (h-HSCT) is a therapeutic option to cure patients affected by hematologic malignancies. The kinetics and the quality of immune-reconstitution (IR) impact the clinical outcome of h-HSCT and limit the onset of life-threatening Human Cytomegalovirus (HCMV) infection/reactivation. Natural Killer (NK) cells are the first lymphocytes that recover after h-HSCT and they can provide rapid innate immune responses against opportunistic pathogens. By performing a longitudinal single-cell analysis of multiparametric flow-cytometry data, we show here that the persistence at high frequencies of CD158b1b2jneg/NKG2Apos/NKG2Cneg/NKp30pos/NKp46pos (KIRneg) NK cells is associated with HCMV infection/reactivation control. These KIRneg NK cells are "unlicensed", and are not terminal-differentiated lymphocytes appearing early during IR and mainly belonging to CD56bright/CD16neg and CD56bright/CD16pos subsets. KIRneg NK cells are enriched in oxidative and glucose metabolism pathways, produce interferon-γ, and are endowed with potent antiviral activity against HCMV ex vivo. Decreased frequencies of KIRneg NK cells early during IR are associated with clinically relevant HCMV replication. Taken together, our findings indicate that the prolonged persistence of KIRneg NK cells after h-HSCT could serve as a biomarker to better predict HCMV infection/reactivation. This phenomenon also paves the way to optimize anti-viral immune responses by enriching post-transplant donor lymphocyte infusions with KIRneg NK cells.

Mammalian Target of Rapamycin Complex 1 Activation in Macrophages Contributes to Persistent Lung Inflammation following Respiratory Tract Viral Infection

Respiratory virus infections cause millions of hospitalizations worldwide each year. Severe infections lead to lung damage that coincides with persistent inflammation and a lengthy repair period. Vaccination and anti-viral therapy help to mitigate severe infections prior to or during the acute stage of disease, but there are currently limited specific treatment options available to individuals suffering from the long-term sequelae of respiratory viral infection. C57BL/6 mice were infected with influenza A/PR/8/34 as a model for severe viral lung infection and allowed to recover for 21 days. Mice were treated with rapamycin, a well characterized mTORC1 inhibitor on days 12 to 20 post infection; a time period after viral clearance. Persistent inflammation following severe influenza infection in mice was primarily driven by macrophages and T cells. UMAP analysis of flow cytometry data revealed that lung macrophages had high activation of mTORC1, an energy sensing kinase involved in inflammatory immune cell effector functions. Rapamycin treatment reduced lung inflammation and the frequency of exudate macrophages, T cells, and B cells in the lung, while not impacting epithelial progenitor cells or adaptive immune memory. These data highlight mTORC1’s role in sustaining persistent inflammation following clearance of a viral respiratory pathogen and suggest a possible intervention for post-viral chronic lung inflammation.

Development of a Machine Learning Decision Support System for Clinical Flow Cytometry

Abstract High parameter flow cytometry is a highly utilized tool in both research and clinical settings for accurate immunophenotyping in immunology, oncology, and many other disciplines. With increasing dimensionality, manual analysis of data becomes more complex and time consuming. Over the past decade, several automated methods have been developed for cell population clustering and dimensionality reduction of high parameter flow cytometry data which have sped up and simplified the discovery of cell populations not observed by manual gating strategies. However, the input and output of such tools are stochastic in nature, thus making their results difficult to reuse with novel samples such as diagnostic patient samples. Several groups have recently attempted to utilize machine learning algorithms to analyze clinical flow cytometry data. These studies focused on training machine learning models with a goal of predicting a specific diagnosis for each sample. This approach can be limiting when the diagnostic features are not perfectly met to predict a specific diagnosis as well as the absence of the patient's clinical data. To our knowledge, no studies have utilized machine learning to identify and classify individual cells based on their raw fluorescence signature to various cellular populations, whether normal or abnormal, to aid a diagnostician to interpret their significance in a clinical context. In this study, we utilized a data set of ~240 million individual cells from ~9,500 flow cytometry files from 2,300 patients to train a machine learning model to predict cell population classifications as annotated by expert hematopathologists. These annotations were implemented into an end-to-end machine learning based decision support system allowing for rapid, automated analysis of high dimensional flow cytometry data with an output aiding the diagnostician in the generation of a diagnosis. A clinical validation highlighted the robustness of this methodology with 100% sensitivity and 94% specificity in the identification of abnormal B-cell populations on a cohort of 1,500 patients. This decision support system also identifies abnormal T-cell and myeloid cell populations with 100% sensitivity. Overall, the decision support system saves significant time as it removes the need for manual gating and analysis, and aids the diagnostician in identifying discrete cell populations and offering descriptive information about any abnormal populations identified.

Best practices for instrument settings and raw data analysis in plant flow cytometry.

Flow cytometry (FCM) is now the most widely used method to determine ploidy levels and genome size of plants. To get reliable estimates and allow reproducibility of measurements, the methodology should be standardized and follow the best practices in the field. In this article, we discuss instrument calibration and quality control and various instrument and acquisition settings (parameters, flow rate, number of events, scales, use of discriminators, peak positions). These settings must be decided before measurements because they determine the amount and quality of the data and thus influence all downstream analyses. We describe the two main approaches to raw data analysis (gating and histogram modeling), and we discuss their advantages and disadvantages. Finally, we provide a summary of best practice recommendations for data acquisition and raw data analysis in plant FCM.