Analysis Of Flow Cytometry Data Research Articles

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.

Minimal residual disease monitoring in B-lineage acute lymphoblastic leukemia using flow cytometry. Guidelines of the Russian-Belarusian multicenter group for pediatric acute leukemia studies

Multicolor flow cytometry is now routinely used in laboratory practice for minimal residual disease (MRD) monitoring in B-cell precursor acute lymphoblastic leukemia (BCP-ALL). This article describes the methodology of MRD detection in BCP-ALL using flow cytometry as recommended by the Russian-Belarusian multicenter group for pediatric acute leukemia studies. This wellharmonized approach includes recommendations for the choice of monoclonal antibodies, sample preparation, cytometer setup, flow cytometry data analysis and interpretation as well as for reporting. These guidelines allow application of multicolor flow cytometry for MRD monitoring in BCP-ALL in children and adults both in local laboratories and in multicenter settings in prospective clinical trials.

B-144 A Comparative Analysis of Unsupervised Machine Learning Algorithms for Polyploidy Analysis Using Flow Cytometry

Abstract Background Recently, there has been a growing interest in using cell differentiation inducers to produce platelets in vitro by inducing megakaryocytic differentiation in cell lines. During this process, cells undergo polyploidization through endomitosis. To measure the degree of polyploidy, cell cycle analysis by flow cytometry is commonly used. However, manual gating can be time-consuming and prone to human error, especially when analyzing large datasets. To address this issue, this study aimed to compare the performance of unsupervised machine learning algorithms for polyploidy analysis of flow cytometry data. Methods The K562 cell line (Korea Cell Line Bank, Korea) was cultured under four conditions, including 1 nM and 2.5 nM of PMA (phorbol 12-myristate 13-acetate; Sigma Aldrich, USA) and 5 μg/mL of phytosphingosine (TCI, Japan), for four days. Propidium iodide (Sigma Aldrich, USA) was used to stain the cells for ploidy analysis, and flow cytometry (Navios, Beckman Coulter Inc, USA) was used to obtain the intensity values. Twelve flow cytometry measurements were obtained for K562 cells cultured under four different conditions, with each condition tested in triplicate. Unsupervised machine learning algorithms, including K-Means, Bisecting K-Means (BKM), Mini Batch K-Means (MBKM), Agglomerative Clustering (AC), Gaussian Mixture Models (GMM), K-Medoids, and Partitioning Around Medoids (PAM), were used to generate boundaries among three clusters (2n, 4n, and ≥8n). The boundary values generated by each algorithm were comparatively analyzed by calculating the standard derivation index (SDI) on each boundary. The concordance of each algorithm with manual gating of flow cytometry data was evaluated by calculating residual errors. Results A total of 168 SDI values were obtained from seven different unsupervised machine learning algorithms for two boundaries among 2n, 4n, and ≥8n clusters of four culture conditions in triplicate. When comparing the average (±95% confidence interval) of SDI for each algorithm for the entire dataset, K-Means showed the most acceptable average SDI value of −0.07 (±0.17) with the narrowest confidence interval followed by −0.13 (±0.54) of AC, −0.26 (±0.18) of MBKM, −0.53 (±0.2) of K-Medoids, −0.58 (±0.2) of PAM, 0.74 (±0.3) of GMM, and 0.84 (±0.35) of BKM. In addition, the average (±95% confidence interval) of the absolute value of the residual errors of each algorithm was calculated, with K-Means showing the lowest value of 6.67 (±2.3), indicating higher concordance with manual gating. AC, MBKM, K-Medoids, PAM, BKM, and GMM had higher average residual errors of 10.04 (±2.5), 10.46 (±4.07), 11.13 (±4.65), 12.04 (±4.83), 12.54 (±3.0), and 12.75 (±3.5), respectively. Conclusion K-means demonstrated the best performance for clustering each ploidy among the seven unsupervised machine learning algorithms tested for the flow cytometry data. In addition, the algorithms achieved acceptable concordance with manual gating in most cases in this study. Therefore, unsupervised machine learning algorithms show promise in automating the gating of flow cytometry data for polyploidy analysis and measuring megakaryocytic differentiation in cell lines.

B-142 Development and Clinical Validation of Artificial Intelligence Assisted Flow Cytometry Diagnosis for Acuteleukemia

Abstract Background Flow cytometry is one of the most commonly used diagnostic tools in hematopathology. Manual flow cytometry data analysis is vulnerable to substantial inter-operator variation and typically takes a technologist 20 min to analyze a case. In the last decade, artificial intelligence (AI) technology has advanced dramatically and become widely used in image processing. Previously, we created an AI-assisted workflow for classifying white blood cell subsets in peripheral blood. The goal of this study is to evaluate the automated flow cytometry analysis system for diagnosing acute leukemia. Methods A total of 117 cases were retrieved from the archived diagnostic cases, consisting of three diagnostic categories: 20 normal cases, 37 acute lymphoblastic leukemia and 60 acute myeloid leukemia. Flow cytometry data manually analyzed by a pathologist is the gold standard. The same flow cytometry data was processed by the flow cytometry analysis software DeepFlow® using proprietary AI algorithm. The final diagnosis and blast cell count of AI and manual analysis are compared by Pearson correlation coefficient. Results The AI-assisted blast cell enumeration showed moderate correlation as compared to manual evaluation (r = 0.8, Fig. 1). Manual adjustment for enumeration of CD34 negative blast population with uncommon immunophenotype is required for 7 cases, including 4 CD34−/CD117+ acute myeloid leukemia and 3 CD34−/TDT+ lymphoblastic leukemia. The AI analysis effectively recognized cell populations and provided correct diagnoses on 94% of cases (110/117). With pathologists’ review, all cases were classified into the correct diagnostic categories. Conclusion Our study demonstrated that AI can accurately recognize the flow cytometry pattern of leukemia and make the proper diagnosis. Additional training for CD34 negative blast gating and other uncommon blast immunophenotypes is being conducted. Ultimately, AI will decrease the demand on manual analysis by technologists and pathologists, standardize the analysis process, and provide accurate diagnosis.

CRUSTY: a versatile web platform for the rapid analysis and visualization of high-dimensional flow cytometry data

Flow cytometry (FCM) can investigate dozens of parameters from millions of cells and hundreds of specimens in a short time and at a reasonable cost, but the amount of data that is generated is considerable. Computational approaches are useful to identify novel subpopulations and molecular biomarkers, but generally require deep expertize in bioinformatics and the use of different platforms. To overcome these limitations, we introduce CRUSTY, an interactive, user-friendly webtool incorporating the most popular algorithms for FCM data analysis, and capable of visualizing graphical and tabular results and automatically generating publication-quality figures within minutes. CRUSTY also hosts an interactive interface for the exploration of results in real time. Thus, CRUSTY enables a large number of users to mine complex datasets and reduce the time required for data exploration and interpretation. CRUSTY is accessible at https://crusty.humanitas.it/.

Abstract 21 Machine Learning Approach for Automatic Enumeration of Cord Blood Stem Cells by Flow Cytometry

Abstract Introduction Stem cell laboratories measure the number of viable CD34+ and CD45+ cells in their products by flow cytometry following a standard protocol (ISHAGE). Although the ISHAGE protocol is well documented, its correct application can be challenging. Automated gating algorithms have been created to address this issue, but they have limitations and can only be used in certain conditions. Recent developments in artificial intelligence (AI) and machine learning (ML) have made these approaches more accessible and practical to use. For flow cytometry data, the Cytobank platform now offers the Automatic Gating Algorithm (AGA), an AI-ML-based feature allowing users to easily implement their own gating strategies. Objectives The performance of the AGA based on the ISHAGE protocol gating was evaluated using cord blood post-thaw flow cytometry data. Methods In this study, the AGA was trained using 29 flow cytometry cord blood data files analyzed according to the ISHAGE gating strategy. Using the model, an inference analysis was conducted on two series of 12 data samples acquired from two independent operators. The enumeration of viable CD34+ cells was manually performed and compared to the AI analyses. Results The mean differences and standard deviation in vCD34+ counts between the manual and the automatic gating are 6.0% ± 2.7 for Operator #1 and 7.7% ± 2.1 for Operator #2. The correlation coefficients between vCD34+ results are 0.9981 for Operator #1 vs AI, and 0.9699 for Operator #2 vs AI. These results show that the AI inference provides results comparable to human manual gating, and are within the accepted difference of 10% between duplicates as defined by ISHAGE convention. Discussion This study provides a testing ground for using AI-ML-based automatic gating and supports its use in stem cell laboratories with acceptable performance and accuracy. The application of AI and ML for vCD34+ enumeration can potentially lead to the development of more widely applicable automatic gating algorithms. This could improve standardization by providing a common tool for analyzing flow cytometry data.

Influenza breakthrough infection in vaccinated mice is characterized by non-pathological lung eosinophilia.

Eosinophils are important mediators of mucosal tissue homeostasis, anti-helminth responses, and allergy. Lung eosinophilia has previously been linked to aberrant Type 2-skewed T cell responses to respiratory viral infection and may also be a consequence of vaccine-associated enhanced respiratory disease (VAERD), particularly in the case of respiratory syncytial virus (RSV) and the formalin-inactivated RSV vaccine. We previously reported a dose-dependent recruitment of eosinophils to the lungs of mice vaccinated with alum-adjuvanted trivalent inactivated influenza vaccine (TIV) following a sublethal, vaccine-matched H1N1 (A/New Caledonia/20/1999; NC99) influenza challenge. Given the differential role of eosinophil subset on immune function, we conducted the investigations herein to phenotype the lung eosinophils observed in our model of influenza breakthrough infection. Here, we demonstrate that eosinophil influx into the lungs of vaccinated mice is adjuvant- and sex-independent, and only present after vaccine-matched sublethal influenza challenge but not in mock-challenged mice. Furthermore, vaccinated and challenged mice had a compositional shift towards more inflammatory eosinophils (iEos) compared to resident eosinophils (rEos), resembling the shift observed in ovalbumin (OVA)-sensitized allergic control mice, however without any evidence of enhanced morbidity or aberrant inflammation in lung cytokine/chemokine signatures. Furthermore, we saw a lung eosinophil influx in the context of a vaccine-mismatched challenge. Additional layers of heterogeneity in the eosinophil compartment were observed via unsupervised clustering analysis of flow cytometry data. Our collective findings are a starting point for more in-depth phenotypic and functional characterization of lung eosinophil subsets in the context of vaccine- and infection-induced immunity.

A topic modeling approach reveals the dynamic T cell composition of peripheral blood during cancer immunotherapy

We present TopicFlow, a computational framework for flow cytometry data analysis of patient blood samples for the identification of functional and dynamic topics in circulating Tcell population. This framework applies a Latent Dirichlet Allocation (LDA) model, adapting the concept of topic modeling in text mining to flow cytometry. To demonstrate the utility of our method, we conducted an analysis of ∼17 million Tcells collected from 138 peripheral blood samples in 51 patients with melanoma undergoing treatment with immune checkpoint inhibitors (ICIs). Our study highlights three latent dynamic topics identified by LDA: a Tcell exhaustion topic that independently recapitulates the previously identified LAG-3+ immunotype associated with ICI resistance, a naive topic and its association with immune-related toxicity, and a Tcell activation topic that emerges upon ICI treatment. Our approach can be broadly applied to mine high-parameter flow cytometry data for insights into mechanisms of treatment response and toxicity.

Guidelines for the flow cytometric minimal residual disease monitoring in B-lineage acute lymphoblastic leukemia after CD19-directed immunotherapy

   Multicolor flow cytometry is now routinely used in laboratory practice for the minimal residual disease (MRD) monitoring in B-cell precursor acute lymphoblastic leukemia (BCP-ALL). Wide application of CD19-directed immunotherapy leads to frequent loss of CD19 expression, that hampers significantly the flow cytometric MRD detection methodology. We developed an antibody panel and data analysis algorithm for multicolor flow cytometry, which is a reliable method for MRD detection in patients with BCP-ALL treated with CD19-directed therapy. We recommend a single-tube 11-color panel for MRD detection, which is adapted for the case of possible CD19 loss. Based on patterns of antigen expression changes and the relative expansion of normal CD19-negative BCPs, guidelines for multicolored flow cytometry data analysis and interpretation are established. The recommended approach is reliable tool for therapy response monitoring displaying the same effectiveness with the more laborious and costly molecular techniques.

A new automatic gating algorithm replicates expert manual gating of immune cell subsets

Abstract Machine learning is increasingly used for the analysis of flow cytometric immunophenotyping data. Many analysis pipelines leverage unsupervised approaches such as dimensionality reduction or clustering, which may lead to populations characterized differently from expert definition. Here we show a new, supervised algorithm for population identification that faithfully replicates the expert manual gating strategy. Peripheral blood samples were drawn from 41 different donors, stained with a dry, unitized B cell panel and acquired on two cytometer units. A manual gating strategy was established, and an automatic model trained on a subset of samples (n=31 donors, split into training, validation and test set) from this study. The performance of the automatic model was evaluated on the 10 unused samples. The automatic identification of populations with differences in median abundance (lymphocytes, CD19+ B-cells and CD19+ CD10+ cells) showed a high level of correlation and lower variability compared to manual gating. The supervised approach presented here retains the gating hierarchy, in contrast to the unsupervised clustering performed by FlowSOM, which splits cells into exclusive metaclusters, leading to metacluster assignments significantly different from expert gating. By automating the identification of populations, assay reproducibility can be increased by removing inter-operator variability. We show that a supervised machine learning tool that supports user-defined panels and gating strategies can reproduce expert analysis, reduce variability of flow cytometry data analysis, and can provide better fit-for-purpose compared to an unsupervised clustering approach.

PyInfinityFlow: Optimized imputation and analysis of high-dimensional Flow Cytometry data for millions of cells.

While conventional flow cytometry is limited to dozens of markers, new experimental and computational strategies, such as Infinity Flow, allow for the generation and imputation of hundreds of cell surface protein markers in millions of cells. Here, we describe an end-to-end analysis workflow for Infinity Flow data in Python. pyInfinityFlow enables the efficient analysis of millions of cells, without down-sampling, through direct integration with well-established Python packages for single-cell genomics analysis. pyInfinityFlow accurately identifies both common and extremely rare cell populations which are challenging to define from single-cell genomics studies alone. We demonstrate that this workflow can nominate novel markers to design new flow cytometry gating strategies for predicted cell populations. pyInfinityFlow can be extended to diverse cell discovery analyses with flexibility to adapt to diverse Infinity Flow experimental designs. pyInfinityFlow is freely available in GitHub (https://github.com/KyleFerchen/pyInfinityFlow) and on PyPI (https://pypi.org/project/pyInfinityFlow/). Package documentation with tutorials on a test dataset is available by Read the Docs (pyinfinityflow.readthedocs.io). The scripts and data for reproducing the results are available at (https://github.com/KyleFerchen/pyInfinityFlow/tree/main/analysis_scripts), along with the raw flow cytometry input data. Supplementary data are available at Bioinformatics online.

Abstract 902: Harnessing CD39 for the treatment of colorectal cancer and liver metastases by engineered T cells

Abstract Colorectal cancer (CRC) is the 2nd cause of cancer-related death. Standard therapies often fail, with more than 50% of patients experiencing relapse, eventually with metastatic disease. Colorectal tumors are densely infiltrated by immune cells that have a role in surveillance and modulation of tumor progression. However, exhaustion mechanisms acting within the tumor microenvironment impede their functional capacity against tumor cells. We paired high-dimensional flow cytometry, RNA sequencing, immunohistochemistry and immunofluorescence to describe the T cell functional landscape in tumor and peritumoral tissues from primary colorectal cancers and liver metastases. Analysis of the healthy, peritumoral and neoplastic tissues of treatment-naïve primary CRCs and of the peritumoral and tumoral tissues of CRC patients undergoing surgery for liver metastasis, revealed extensive transcriptional and spatial remodeling across tumors, being metabolic pathways among the major drivers of this variance. Regarding the immune infiltrate, we found that T cells are mainly localized at the front edge and that tumor-infiltrating T cells co-express multiple inhibitory receptors. Unsupervised analysis of flow cytometry data performed by an advanced pipeline of data handling by dimensionality reduction and clustering algorithms allowed the definition of a peculiar inhibitory receptors signature in TILs enriched both in primary CRCs and liver metastases. Among the highly co-expressed inhibitory receptors, CD39 was found to represent the major driver of exhaustion in both primary and metastatic colorectal tumors. CD39 is a diphosphohydrolase converting ATP into AMP that is emerging as exhaustion marker for tumor-specific T cells, thus highlighting its relevance as molecular target for T cells engineering. We leveraged on these findings to generate a novel cellular product for the adoptive cell therapy of CRC. By CRISPR/Cas9 genome editing tools, we simultaneously redirected T cell specificity by disrupting the alpha and beta genes of the endogenous T cell receptor with 90% efficiency for both genes, and disrupted CD39 with 100% efficiency, generating triple-knockout engineered lymphocytes. By lentiviral transduction, we redirected the specificity of our engineered T cell product employing a novel T-cell receptor targeting the HER-2 antigen. Triple-edited, HER2-redirected T cells were challenged in vitro against HER2+ patient-derived organoids from liver metastases (mPDOs). Measurement of Cas3/7 displayed a functional advantage for CD39-disrupted, HER2-redirected T cells in recognizing and killing mPDOs. We then evaluated the efficacy of our T cell product in two different in vivo models: subcutaneous injection of mPDOs, and intra-hepatic injection of mPDOs. In both models, CD39-disrupted, HER2-redirected T cells displayed a superior capacity of controlling tumor outgrowth long term. Citation Format: Alessia Potenza, Chiara Balestrieri, Luca Albarello, Federica Pedica, Martina Spiga, Francesco Manfredi, Beatrice C. Cianciotti, Claudia De Lalla, Lorena Stasi, Elena Tassi, Silvia Bonfiglio, Giulia M. Scotti, Miriam Redegalli, Donatella Biancolini, Danilo Abbati, Fabio Simeoni, Dejan Lazarevic, Ugo Elmore, Guido Fiorentini, Giulia Di Lullo, Giulia Casorati, Claudio Doglioni, Giovanni Tonon, Paolo Dellabona, Riccardo Rosati, Luca Aldrighetti, Eliana Ruggiero, Chiara Bonini. Harnessing CD39 for the treatment of colorectal cancer and liver metastases by engineered T cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 902.

Deep Immunophenotyping of Human Whole Blood by Standardized Multi-parametric Flow Cytometry Analyses

Immunophenotyping is proving crucial to understanding the role of the immune system in health and disease. High-throughput flow cytometry has been used extensively to reveal changes in immune cell composition and function at the single-cell level. Here, we describe six optimized 11-color flow cytometry panels for deep immunophenotyping of human whole blood. A total of 51 surface antibodies, which are readily available and validated, were selected to identify the key immune cell populations and evaluate their functional state in a single assay. The gating strategies for effective flow cytometry data analysis are included in the protocol. To ensure data reproducibility, we provide detailed procedures in three parts, including (1) instrument characterization and detector gain optimization, (2) antibody titration and sample staining, and (3) data acquisition and quality checks. This standardized approach has been applied to a variety of donors for a better understanding of the complexity of the human immune system.

Management of genetic erosion: The (successful) case study of the pear (Pyrus communis L.) germplasm of the Lazio region (Italy).

Pyrus communis L. is an important temperate fruit with high nutritional and economic value. Italy, as the largest pear producer in the EU and second in the world, has a particularly rich germplasm characterized by hundreds of local varieties. The Lazio Region was the first Italian region to start programs focused on safeguarding varieties at risk of extinction and has started a massive census of fruit varieties by combining molecular data and productive-territorial information. In this study, 311 pear accessions collected from the five provinces of the Lazio region were genetically characterized by the means of nine simple sequence repeat (SSR) markers, eight of which were recommended by the European Cooperative Programme for Plant Genetic Resources (ECPGR). The SSR analysis revealed 250 unique genotypes and 14 cases of synonymies, namely, accessions with different names but identical molecular profiles (100% genetic similarity). The microsatellite set showed a high polymorphism information content (PIC; mean PIC=0.77) and an exceptionally high discrimination power (DP = 0.99), making it particularly efficient both for the study of genetic diversity and for the prediction of the degree of ploidy. Notably, 20% of the accessions displayed triallelic profiles (i.e., accessions having ≥2 loci with a third allele), while the remaining accessions were diploids. These results were further confirmed by flow cytometry data analysis. Standardization of the molecular analyses at the national and international levels and harmonization of the SSR sets used for germplasm characterization are of paramount importance for producing molecular profiles useful for registration and variety maintenance.

Analyzing the Tumor-Immune Microenvironment by Flow Cytometry.

Flow cytometry is an essential tool for studying the tumor-immune microenvironment. It allows us to quickly quantify and identify multiple cell types in a heterogeneous sample. This chapter provides an overview of the flow cytometry instrumentation and a discussion of the appropriate considerations and steps in building a reproducible flow cytometry staining panel. We present an updated lymphoid tissue and solid tumor-infiltrating leucocyte flow cytometry staining protocol and an example of flow cytometry data analysis.