In Flow Cytometry, Image Is Everything

Machine learning implementation strategy in imaging and impedance flow cytometry

Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.

Visualisation of Host-Pathogen Communication

The core of biomedical science is the use of laboratory techniques to support the diagnosis and treatment of disease in clinical settings. Despite tremendous advancement in our understanding of medicine in recent years, we are still far from having a complete understanding of human physiology in homeostasis, let alone the pathology of disease states. Indeed medical advances over the last two hundred years would not have been possible without the invention of and continuous development of visualisation techniques available to research scientists and clinicians. As we have all learned from the recent COVID pandemic, despite advances in modern medicine we still have much to learn regarding infection biology. Indeed antimicrobial resistant (AMR) bacteria are a global threat to human health, meaning research into bacterial pathogenesis is vital. In this chapter, we will briefly describe the nature of microbes and host immune responses before delving into some of the visualisation techniques utilised in the field of biomedical research with a focus on host-pathogen interactions. We will give a brief overview of commonly used techniques from gold standard staining methods, in situ hybridisation, microscopy, western blotting, microbial characterisation, to cutting-edge image flow cytometry and mass spectrometry. Specifically, we will focus on techniques utilised to visualise interactions between the host, our own bodies, and invading organisms including bacteria. We will touch on in vitro and ex vivo modelling methodology with examples utilised to delineate pathogenicity in disease. A better understanding of bacterial biology, immunology and how these fields interact (host-pathogen communications) in biomedical research is integral to developing novel therapeutic approaches which circumvent the need for antibiotics, an important issue as we enter a post-antibiotic era.

Diagnosis of Acute Leukemia by Multiparameter Flow Cytometry with the Assistance of Artificial Intelligence

We developed an artificial intelligence (AI) model that evaluates the feasibility of AI-assisted multiparameter flow cytometry (MFC) diagnosis of acute leukemia. Two hundred acute leukemia patients and 94 patients with cytopenia(s) or hematocytosis were selected to study the AI application in MFC diagnosis of acute leukemia. The kappa test analyzed the consistency of the diagnostic results and the immunophenotype of acute leukemia. Bland–Altman and Pearson analyses evaluated the consistency and correlation of the abnormal cell proportion between the AI and manual methods. The AI analysis time for each case (83.72 ± 23.90 s, mean ± SD) was significantly shorter than the average time for manual analysis (15.64 ± 7.16 min, mean ± SD). The total consistency of diagnostic results was 0.976 (kappa (κ) = 0.963). The Bland–Altman evaluation of the abnormal cell proportion between the AI analysis and manual analysis showed that the bias ± SD was 0.752 ± 6.646, and the 95% limit of agreement was from −12.775 to 13.779 (p = 0.1225). The total consistency of the AI immunophenotypic diagnosis and the manual results was 0.889 (kappa, 0.775). The consistency and speedup of the AI-assisted workflow indicate its promising clinical application.

Image-Based Live Cell Sorting

Technologies capable of cell separation based on cell images provide powerful tools enabling cell selection criteria that rely on spatially or temporally varying properties. Image-based cell sorting (IBCS) systems utilize microfluidic or microarray platforms, each having unique characteristics and applications. The advent of IBCS marks a new paradigm in which cell phenotype and behavior can be explored with high resolution and tied to cellular physiological and omics data, providing a deeper understanding of single-cell physiology and the creation of cell lines with unique properties. Cell sorting guided by high-content image information has far-reaching implications in biomedical research, clinical medicine, and pharmaceutical development.

Diffractive Beam Shaper for Multiwavelength Lasers for Flow Cytometry

Illumination spot in a flow cytometer is a crucial factor determining the measurement accuracy and stability. The traditional mechanism is to precisely calibrate multiple optical components to convert circular Gaussian beams into elliptical Gaussian beams, making it difficult to shape multiwavelength lasers simultaneously. A diffractive beam shaper for multicolor lasers with high simplicity, only containing one diffractive optical element and one focusing lens is created in this work. It can produce rectangular spots, of which the number, the sizes, and the positions are accurately determined by the incident wavelengths. Demonstrated in the customized microflow cytometer, the coefficient of variations (CV) of the optical signals by the beam shaper are 3.6-6.5%, comparable to those derived from the commercial instrument with 3.3-6.3% CVs. Benefiting from the narrow rectangular spots and the flexibility of diffractively shaped lasers, the measurement of bead sizes with 4-15 μm diameters and the real-time detection of flow velocity from 0.79 to 9.50 m/s with the CV of <5% are achieved. © 2020 International Society for Advancement of Cytometry.