Anterior uveitis is the most common form of intraocular inflammation, and one of its main signs is the presence of white blood cells (WBCs) in the anterior chamber (AC). Clinically, the true composition of cells can currently only be obtained using AC paracentesis, an invasive procedure to obtain AC fluid requiring needle insertion into the AC. We previously developed a spectroscopic optical coherence tomography (SOCT) analysis method to differentiate between populations of RBCs and subtypes of WBCs, including granulocytes, lymphocytes and monocytes, both
- NSF-PAR ID:
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Biomedical Optics Express
- Page Range / eLocation ID:
- Article No. 2134
- Medium: X
- Sponsoring Org:
- National Science Foundation
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N= 11 healthy, N= 5 lung and kidney cancer) present a unique disposable cell counter, demonstrating the ability of this tool to monitor neutrophil and WBC counts within clinical or in resource‐constrained environments.
White blood cell (WBC) differential counting is an established clinical routine to assess patient immune system status. Fluorescent markers and a flow cytometer are required for the current state‐of‐the‐art method for determining WBC differential counts. However, this process requires several sample preparation steps and may adversely disturb the cells. We present a novel label‐free approach using an imaging flow cytometer and machine learning algorithms, where live, unstained WBCs were classified. It achieved an average F1‐score of 97% and two subtypes of WBCs, B and T lymphocytes, were distinguished from each other with an average F1‐score of 78%, a task previously considered impossible for unlabeled samples. We provide an open‐source workflow to carry out the procedure. We validated the WBC analysis with unstained samples from 85 donors. The presented method enables robust and highly accurate identification of WBCs, minimizing the disturbance to the cells and leaving marker channels free to answer other biological questions. It also opens the door to employing machine learning for liquid biopsy, here, using the rich information in cell morphology for a wide range of diagnostics of primary blood. © 2019 The Authors.
Cytometry Part Apublished by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
Profiling circulating tumour cells (CTCs) in cancer patients' blood samples is critical to understand the complex and dynamic nature of metastasis. This task is challenged by the fact that CTCs are not only extremely rare in circulation but also highly heterogeneous in their molecular programs and cellular functions. Here we report a combinational approach for the simultaneous biochemical and functional phenotyping of patient-derived CTCs, using an integrated inertial ferrohydrodynamic cell separation (i 2 FCS) method and a single-cell microfluidic migration assay. This combinatorial approach offers unique capability to profile CTCs on the basis of their surface expression and migratory characteristics. We achieve this using the i 2 FCS method that successfully processes whole blood samples in a tumor cell marker and size agnostic manner. The i 2 FCS method enables an ultrahigh blood sample processing throughput of up to 2 × 10 5 cells s −1 with a blood sample flow rate of 60 mL h −1 . Its short processing time (10 minutes for a 10 mL sample), together with a close-to-complete CTC recovery (99.70% recovery rate) and a low WBC contamination (4.07-log depletion rate by removing 99.992% of leukocytes), results in adequate and functional CTCs for subsequent studies in the single-cell migration device. For the first time, we employ this new approach to query CTCs with single-cell resolution in accordance with their expression of phenotypic surface markers and migration properties, revealing the dynamic phenotypes and the existence of a high-motility subpopulation of CTCs in blood samples from metastatic lung cancer patients. This method could be adopted to study the biological and clinical value of invasive CTC phenotypes.more » « less
Objective and Impact Statement . We present a fully automated hematological analysis framework based on single-channel (single-wavelength), label-free deep-ultraviolet (UV) microscopy that serves as a fast, cost-effective alternative to conventional hematology analyzers. Introduction . Hematological analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel, costly chemical reagents, and lengthy protocols. Label-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler workflow. In this work, we leverage the unique capabilities of deep-UV microscopy as a label-free, molecular imaging technique to develop a deep learning-based pipeline that enables virtual staining, segmentation, classification, and counting of white blood cells (WBCs) in single-channel images of peripheral blood smears. Methods . We train independent deep networks to virtually stain and segment grayscale images of smears. The segmented images are then used to train a classifier to yield a quantitative five-part WBC differential. Results. Our virtual staining scheme accurately recapitulates the appearance of cells under conventional Giemsa staining, the gold standard in hematology. The trained cellular and nuclear segmentation networks achieve high accuracy, and the classifier can achieve a quantitative five-part differential on unseen test data. Conclusion . This proposed automated hematology analysis framework could greatly simplify and improve current complete blood count and blood smear analysis and lead to the development of a simple, fast, and low-cost, point-of-care hematology analyzer.more » « less