Purpose: The aim of this study was to determine the prognostic value of the chemokine CCL5, considered as a promalignancy factor in breast cancer, in predicting breast cancer progression and to evaluate its ability to strengthen the prognostic significance of other biomarkers. Experimental Design: The expression of CCL5, alone and in conjunction with estrogen receptor (ER)-α, ER-β, progesterone receptor (PR), and HER-2/neu (ErbB2), was determined in breast tumor cells by immunohistochemistry. The study included 142 breast cancer patients, including individuals in whom disease has progressed. Results: Using Cox proportional hazard models, univariate analysis suggested that, in stage I breast cancer patients, CCL5 was not a significant predictor of disease progression. In contrast, in stage II patients, the expression of CCL5 (CCL5+), the absence of ER-α (ER-α−), and the lack of PR expression (PR−) increased significantly the risk for disease progression (P = 0.0045, 0.0041, and 0.0107, respectively). The prognostic strength of CCL5, as well as of ER-α−, improved by combining them together (CCL5+/ER-α−: P = 0.0001), being highly evident in the stage IIA subgroup [CCL5+/ER-α− (P = 0.0003); ER-α− (P = 0.0315)]. In the stage II group as a whole, the combinations of CCL5−/ER-α+ and CCL5−/PR+ were highly correlated with an improved prognosis. Multivariate analysis indicated that, in stage II patients, ER-α and CCL5 were independent predictors of disease progression. Conclusions: CCL5 could be considered as a biomarker for disease progression in stage II breast cancer patients, with the CCL5+/ER-α− combination providing improved prediction of disease progression, primarily in the stage IIA subgroup.

A major challenge in the field of optical imaging of live cells is achieving rapid, 3D, and noninvasive imaging of isolated cells without labeling. If successful, many clinical procedures involving analysis and sorting of cells drawn from body fluids, including blood, can be significantly improved. A new label‐free tomographic interferometry approach is presented. This approach provides rapid capturing of the 3D refractive‐index distribution of single cells in suspension. The cells flow in a microfluidic channel, are trapped, and then rapidly rotated by dielectrophoretic forces in a noninvasive and precise manner. Interferometric projections of the rotated cell are acquired and processed into the cellular 3D refractive‐index map. Uniquely, this approach provides full (360°) coverage of the rotation angular range around any axis, and knowledge on the viewing angle. The experimental demonstrations presented include 3D, label‐free imaging of cancer cells and three types of white blood cells. This approach is expected to be useful for label‐free cell sorting, as well as for detection and monitoring of pathological conditions resulting in cellular morphology changes or occurrence of specific cell types in blood or other body fluids.

We present a new acquisition method that enables high-resolution, fine-detail full reconstruction of the three-dimensional movement and structure of individual human sperm cells swimming freely. We achieve both retrieval of the three-dimensional refractive-index profile of the sperm head, revealing its fine internal organelles and time-varying orientation, and the detailed fourdimensional localization of the thin, highly-dynamic flagellum of the sperm cell. Live human sperm cells were acquired during free swim using a high-speed off-axis holographic system that does not require any moving elements or cell staining. The reconstruction is based solely on the natural movement of the sperm cell and a novel set of algorithms, enabling the detailed fourdimensional recovery. Using this refractive-index imaging approach, we believe we have detected an area in the cell that is attributed to the centriole. This method has great potential for both biological assays and clinical use of intact sperm cells.

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Many medical and biological protocols for analyzing individual biological cells involve morphological evaluation based on cell staining, designed to enhance imaging contrast and enable clinicians and biologists to differentiate between various cell organelles. However, cell staining is not always allowed in certain medical procedures. In other cases, staining may be time consuming or expensive to implement. Furthermore, staining protocols may be operator-sensitive, and hence lead to varying analytical results by different users, as well as cause artificial imaging artifacts or false heterogeneity. Here, we present a new deep-learning approach, called HoloStain, which converts images of isolated biological cells acquired without staining by holographic microscopy to their virtually stained images. We demonstrate this approach for human sperm cells, as there is a well-established protocol and global standardization for characterizing the morphology of stained human sperm cells for fertility evaluation, but, on the other hand, staining might be cytotoxic and thus is not allowed during human in vitro fertilization (IVF). We use deep convolutional Generative Adversarial Networks (DCGANs) with training that is based on both the quantitative phase images and two gradient phase images, all extracted from the digital holograms of the stain-free cells, with the ground truth of bright-field images of the same cells that subsequently underwent chemical staining. After the training stage, the deep neural network can take images of unseen sperm cells, retrieved from the coinciding holograms acquired without staining, and convert them to their stain-like images. To validate the quality of our virtual staining approach, an experienced embryologist analyzed the unstained cells, the virtually stained cells, and the chemically stained sperm cells several times in a blinded and randomized manner. We obtained a 5-fold recall (sensitivity) improvement in the analysis results, demonstrating the advantage of using virtual staining for sperm cell analysis. With the introduction of simple holographic imaging methods in clinical settings, the proposed method has a great potential to become a common practice in human IVF procedures, as well as to significantly simplify and facilitate other cell analyses and techniques such as imaging flow cytometry.Submitted