Cell Segmentation for Image Cytometry: Advances, Insufficiencies, and Challenges

An approach of separating the overlapped cells or nuclei based on the outer Canny edges and morphological erosion

In biomedicine, the automatic processing of medical microscope images plays a key role in the subsequent analysis and diagnosis. Cell or nucleus segmentation is one of the most challenging tasks for microscope image processing. Due to the frequently occurred overlapping, few segmentation methods can achieve satisfactory segmentation accuracy yet. In this paper, we propose an approach to separate the overlapped cells or nuclei based on the outer Canny edges and morphological erosion. The threshold selection is first used to segment the foreground and background of cell or nucleus images. For each binary connected domain in the segmentation image, an intersection based edge selection method is proposed to choose the outer Canny edges of the overlapped cells or nuclei. The outer Canny edges are used to generate a binary cell or nucleus image that is then used to compute the cell or nucleus seeds by the proposed morphological erosion method. The nuclei of the Human U2OS cells, the mouse NIH3T3 cells and the synthetic cells are used for evaluating our proposed approach. The quantitative quantification accuracy is computed by the Dice score and 95.53% is achieved by the proposed approach. Both the quantitative and the qualitative comparisons show that the accuracy of the proposed approach is better than those of the area constrained morphological erosion (ACME) method, the iterative erosion (IE) method, the morphology and watershed (MW) method, the Generalized Laplacian of Gaussian filters (GLGF) method and ellipse fitting (EF) method in separating the cells or nuclei in three publicly available datasets.

Novel insights into Hodgkin lymphoma biology by single-cell analysis

The emergence and rapid development of single-cell technologies mark a paradigm shift in cancer research. Various technology implementations represent powerful tools to understand cellular heterogeneity, identify minor cell populations that were previously hard to detect and define, and make inferences about cell-to-cell interactions at single-cell resolution. Applied to lymphoma, recent advances in single-cell RNA sequencing have broadened opportunities to delineate previously underappreciated heterogeneity of malignant cell differentiation states and presumed cell of origin, and to describe the composition and cellular subsets in the ecosystem of the tumor microenvironment (TME). Clinical deployment of an expanding armamentarium of immunotherapy options that rely on targets and immune cell interactions in the TME emphasizes the requirement for a deeper understanding of immune biology in lymphoma. In particular, classic Hodgkin lymphoma (CHL) can serve as a study paradigm because of its unique TME, featuring infrequent tumor cells among numerous nonmalignant immune cells with significant interpatient and intrapatient variability. Synergistic to advances in single-cell sequencing, multiplexed imaging techniques have added a new dimension to describing cellular cross talk in various lymphoma entities. Here, we comprehensively review recent progress using novel single-cell technologies with an emphasis on the TME biology of CHL as an application field. The described technologies, which are applicable to peripheral blood, fresh tissues, and formalin-fixed samples, hold the promise to accelerate biomarker discovery for novel immunotherapeutic approaches and to serve as future assay platforms for biomarker-informed treatment selection, including immunotherapies.

Imaging Mass Cytometry in Immuno-Oncology

In situ profiling of the tumor-immune microenvironment (TiME) requires the ability to co-localize and detect multiple proteins simultaneously. Imaging mass cytometry (IMC), using the Hyperion™ imaging system is a novel multiplex imaging modality that currently enables detection of up to 50 markers on fixed tissues at subcellular resolution and thus has the potential to inform both pre-clinical and clinical research by providing investigators with spatially resolved information about the TiME. Here we provide an overview of the IMC workflow from sample fixation to analysis, with a focus on multiplex panel design and tissue staining.

Multiplexed-Based Assessment of DNA Damage Response to Chemotherapies Using Cell Imaging Cytometry

The current methods for measuring the DNA damage response (DDR) are relatively labor-intensive and usually based on Western blotting, flow cytometry, and/or confocal immunofluorescence analyses. They require many cells and are often limited to the assessment of a single or few proteins. Here, we used the Celigo^® image cytometer to evaluate the cell response to DNA-damaging agents based on a panel of biomarkers associated with the main DDR signaling pathways. We investigated the cytostatic or/and the cytotoxic effects of these drugs using simultaneous propidium iodide and calcein-AM staining. We also describe new dedicated multiplexed protocols to investigate the qualitative (phosphorylation) or the quantitative changes of eleven DDR markers (H2AX, DNA-PKcs, ATR, ATM, CHK1, CHK2, 53BP1, NBS1, RAD51, P53, P21). The results of our study clearly show the advantage of using this methodology because the multiplexed-based evaluation of these markers can be performed in a single experiment using the standard 384-well plate format. The analyses of multiple DDR markers together with the cell cycle status provide valuable insights into the mechanism of action of investigational drugs that induce DNA damage in a time- and cost-effective manner due to the low amounts of antibodies and reagents required.

Cell segmentation in imaging-based spatial transcriptomics

Single-molecule spatial transcriptomics protocols based on in situ sequencing or multiplexed RNA fluorescent hybridization can reveal detailed tissue organization. However, distinguishing the boundaries of individual cells in such data is challenging and can hamper downstream analysis. Current methods generally approximate cells positions using nuclei stains. We describe a segmentation method, Baysor, that optimizes two-dimensional (2D) or three-dimensional (3D) cell boundaries considering joint likelihood of transcriptional composition and cell morphology. While Baysor can take into account segmentation based on co-stains, it can also perform segmentation based on the detected transcripts alone. To evaluate performance, we extend multiplexed error-robust fluorescence in situ hybridization (MERFISH) to incorporate immunostaining of cell boundaries. Using this and other benchmarks, we show that Baysor segmentation can, in some cases, nearly double the number of cells compared to existing tools while reducing segmentation artifacts. We demonstrate that Baysor performs well on data acquired using five different protocols, making it a useful general tool for analysis of imaging-based spatial transcriptomics.

A generic approach for cell segmentation based on Gabor filtering and area-constrained ultimate erosion

Nowadays, the demand for segmenting different types of cells imaged by microscopes is increased tremendously. The requirements for the segmentation accuracy are becoming stricter. Because of the great diversity of cells, no traditional methods could segment various types of cells with adequate accuracy. In this paper, we aim to propose a generic approach that is capable of segmenting various types of cells robustly and counting the total number of cells accurately. To this end, we utilize the gradients of cells instead of intensity for cell segmentation because the gradients are less affected by the global intensity variations. To improve the segmentation accuracy, we utilize the Gabor filter to increase the intensity uniformity of the gradient image. To get the optimal segmentation, we utilize the slope difference distribution based threshold selection method to segment the Gabor filtered gradient image. At last, we propose an area-constrained ultimate erosion method to separate the connected cells robustly. Twelve types of cells are used to test the proposed approach in this paper. Experimental results showed that the proposed approach is very promising in meeting the strict accuracy requirements for many applications.

Microfluidic Single-Cell Analytics

What is the impact of cellular heterogeneity on process performance? How do individual cells contribute to averaged process productivity? Single-cell analysis is a key technology for answering such key questions of biotechnology, beyond bulky measurements with populations. The analysis of cellular individuality, its origins, and the dependency of process performance on cellular heterogeneity has tremendous potential for optimizing biotechnological processes in terms of metabolic, reaction, and process engineering. Microfluidics offer unmatched environmental control of the cellular environment and allow massively parallelized cultivation of single cells. However, the analytical accessibility to a cell's physiology is of crucial importance for obtaining the desired information on the single-cell production phenotype. Highly sensitive analytics are required to detect and quantify the minute amounts of target analytes and small physiological changes in a single cell. For their application to biotechnological questions, single-cell analytics must evolve toward the measurement of kinetics and specific rates of the smallest catalytic unit, the single cell. In this chapter, we focus on an introduction to the latest single-cell analytics and their application for obtaining physiological parameters in a biotechnological context from single cells. We present and discuss recent advancements in single-cell analytics that enable the analysis of cell-specific growth, uptake, and production kinetics, as well as the gene expression and regulatory mechanisms at a single-cell level.