Artificial intelligence in clinical multiparameter flow cytometry and mass cytometry-key tools and progress

Single-cell omics technologies - Fundamentals on how to create single-cell looking glasses for reproductive health

Over the last decade, in line with the goals of precision medicine to offer individualized patient care, various single-cell technologies measuring gene and proteomic expression in various tissues have rapidly advanced to study health and disease at the single cell level. Precisely understanding cell composition, position within tissues, signaling pathways, and communication can reveal insights into disease mechanisms and systemic changes during development, pregnancy, and gynecologic disorders across the lifespan. Single-cell technologies dissect the complex cellular compositions of reproductive tract tissues, providing insights into mechanisms behind reproductive tract dysfunction which impact wellness and quality of life. These technologies aim to understand basic tissue and organ functions and, clinically, to develop novel diagnostics, early disease biomarkers, and cell-targeted therapies for currently suboptimally-treated disorders. Increasingly, they are applied to pregnancy and pregnancy disorders, gynecologic malignancies, and uterine and ovarian physiology and aging, which are discussed in more detail in manuscripts in this special issue of AJOG. Here, we review recent applications of single-cell technologies to the study of gynecologic disorders and systemic biological adaptations during fetal development, pregnancy, and across a woman's lifespan. We discuss sequencing- and proteomic-based single-cell methods, as well as spatial transcriptomics and high-dimensional proteomic imaging, describing each technology's mechanism, workflow, quality control, and highlighting specific benefits, drawbacks, and utility in the context of reproductive medicine. We consider analytical methods for the high-dimensional single-cell data generated, highlighting statistical constraints and recent computational techniques for downstream clinical translation. Overall, current and evolving single-cell "looking glasses", or perspectives, have the potential to transform fundamental understanding of women's health and reproductive disorders and alter the trajectory of clinical practice and patient outcomes in the future.

Acute Leukemia of Ambiguous Lineage: Diagnosis and Evaluation by Flow Cytometry

Acute leukemia of ambiguous lineage (ALAL) includes mixed-phenotype acute leukemia (MPAL), which exhibits immunophenotypic evidence of differentiation along more than one cell lineage, and acute undifferentiated leukemia (AUL), which lacks sufficient immunophenotypic differentiation along any cell lineage. This review provides an overview of ALAL, emphasizing the central role of flow cytometric analysis in its diagnostic workflow. It primarily focuses on MPAL, addressing updated classification and diagnostic criteria by the WHO-HEM5 and the ICC, including both genetically defined and phenotypically defined MPAL. The article provides a detailed review of the MPAL lineage assignment criteria with an illustrative description of a series of MPAL cases. Future studies are needed to reconcile the different criteria used in these two classifications. Continuously expanded molecular studies are expected to provide a genomic and lineage-associated framework for the classification of ALAL with clinical relevance in the diagnosis and therapy selection.

Data Flow-Based Strategies to Improve the Interpretation and Understanding of Machine Learning Models

Data flow-based strategies that seek to improve the understanding of A.I.-based results are examined here by carefully curating and monitoring the flow of data into, for example, artificial neural networks and random forest supervised models. While these models possess structures and related fitting procedures that are highly complex, careful restriction of the data being utilized by these models can provide insight into how they interpret data structures and associated variables sets and how they are affected by differing levels of variation in the data. The goal is improving our understanding of A.I.-based supervised modeling-based results and their stability across different data sources. Some guidelines are suggested for such first-stage adjustments and related data issues.

Application of Mass Cytometry Platforms to Solid Organ Transplantation

Transplantation serves as the cornerstone of treatment for patients with end-stage organ disease. The prevalence of complications, such as allograft rejection, infection, and malignancies, underscores the need to dissect the complex interactions of the immune system at the single-cell level. In this review, we discuss studies using mass cytometry or cytometry by time-of-flight, a cutting-edge technology enabling the characterization of immune populations and cell-to-cell interactions in granular detail. We review the application of mass cytometry in human and experimental animal studies in the context of transplantation, uncovering invaluable contributions of the tool to understanding rejection and other transplant-related complications. We discuss recent innovations that have the potential to streamline and standardize mass cytometry workflows for application to multisite clinical trials. Additionally, we introduce imaging mass cytometry, a technique that couples the power of mass cytometry with spatial context, thereby mapping cellular interactions within tissue microenvironments. The synergistic integration of mass cytometry and imaging mass cytometry data with other omics data sets and high-dimensional data platforms to further define immune dynamics is discussed. In conclusion, mass cytometry technologies, when integrated with other tools and data, shed light on the intricate landscape of the immune response in transplantation. This approach holds significant potential for enhancing patient outcomes by advancing our understanding and facilitating the development of new diagnostics and therapeutics.

Recommendations for using artificial intelligence in clinical flow cytometry

Flow cytometry is a key clinical tool in the diagnosis of many hematologic malignancies and traditionally requires close inspection of digital data by hematopathologists with expert domain knowledge. Advances in artificial intelligence (AI) are transferable to flow cytometry and have the potential to improve efficiency and prioritization of cases, reduce errors, and highlight fundamental, previously unrecognized associations with underlying biological processes. As a multidisciplinary group of stakeholders, we review a range of critical considerations for appropriately applying AI to clinical flow cytometry, including use case identification, low and high risk use cases, validation, revalidation, computational considerations, and the present regulatory frameworks surrounding AI in clinical medicine. In particular, we provide practical guidance for the development, implementation, and suggestions for potential regulation of AI-based methods in the clinical flow cytometry laboratory. We expect these recommendations to be a helpful initial framework of reference, which will also require additional updates as the field matures.

Translating the regulatory landscape of medical devices to create fit-for-purpose artificial intelligence (AI) cytometry solutions

The implementation of medical software and artificial intelligence (AI) algorithms into routine clinical cytometry diagnostic practice requires a thorough understanding of regulatory requirements and challenges throughout the cytometry software product lifecycle. To provide cytometry software developers, computational scientists, researchers, industry professionals, and diagnostic physicians/pathologists with an introduction to European Union (EU) and United States (US) regulatory frameworks. Informed by community feedback and needs assessment established during two international cytometry workshops, this article provides an overview of regulatory landscapes as they pertain to the application of AI, AI-enabled medical devices, and Software as a Medical Device in diagnostic flow cytometry. Evolving regulatory frameworks are discussed, and specific examples regarding cytometry instruments, analysis software and clinical flow cytometry in-vitro diagnostic assays are provided. An important consideration for cytometry software development is the modular approach. As such, modules can be segregated and treated as independent components based on the medical purpose and risk and become subjected to a range of context-dependent compliance and regulatory requirements throughout their life cycle. Knowledge of regulatory and compliance requirements enhances the communication and collaboration between developers, researchers, end-users and regulators. This connection is essential to translate scientific innovation into diagnostic practice and to continue to shape the development and revision of new policies, standards, and approaches.

Immune profiling of age and adjuvant-specific activation of human blood mononuclear cells in vitro

Vaccination reduces morbidity and mortality due to infections, but efficacy may be limited due to distinct immunogenicity at the extremes of age. This raises the possibility of employing adjuvants to enhance immunogenicity and protection. Early IFNγ production is a hallmark of effective vaccine immunogenicity in adults serving as a biomarker that may predict effective adjuvanticity. We utilized mass cytometry (CyTOF) to dissect the source of adjuvant-induced cytokine production in human blood mononuclear cells (BMCs) from newborns (~39-week-gestation), adults (~18-63 years old) and elders (>65 years of age) after stimulation with pattern recognition receptors agonist (PRRa) adjuvants. Dimensionality reduction analysis of CyTOF data mapped the BMC compartment, elucidated age-specific immune responses and profiled PRR-mediated activation of monocytes and DCs upon adjuvant stimulation. Furthermore, we demonstrated PRRa adjuvants mediated innate IFNγ induction and mapped NK cells as the key source of TLR7/8 agonist (TLR7/8a) specific innate IFNγ responses. Hierarchical clustering analysis revealed age and TLR7/8a-specific accumulation of innate IFNγ producing γδ T cells. Our study demonstrates the application of mass cytometry and cutting-edge computational approaches to characterize immune responses across immunologically distinct age groups and may inform identification of the bespoke adjuvantation systems tailored to enhance immunity in distinct vulnerable populations.

Promises and Remaining Challenges for Further Integration of Basophil Activation Test in Allergy-Related Research and Clinical Practice

More than 20 years after having been initially proposed, the relevance and usefulness of basophil activation test (BAT) for the field of allergy research and testing were demonstrated on many occasions. Leveraging the fully open format of a flexible, whole blood-based functional assay, BAT has been shown to be equally important for fundamental research, clinical research, and diagnosis. Regardless of whether the focus of a study is on the characterization of the allergenic moiety, on the patient side, or on the study of the fundamental processes involved in the allergic disease or its treatment, BAT enables the gathering of very important insights. In spite of this, its full capabilities have yet to be leveraged. Various bottlenecks, including but not limited to assay logistics, robustness, flow cytometry access, and/or expertise, have indeed been limiting its development beyond experts and long-term users. Now, various initiatives, aiming at resolving these bottlenecks, have been launched. If successful, a broader use of BAT could then be contemplated. In such a situation, its more thorough integration in clinical practice has the potential to significantly change the allergic patient's journey.