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Background: Flow cytometry has been widely used for the diagnosis of various hematopoietic diseases. Although there have been advances in the number of biomarkers that can be analyzed simultaneously and technologies that enable fast performance, the diagnostic data are still interpreted by a manual gating strategy. The process is labor-intensive, time-consuming, and subject to human error., Results: We used 80 sets of flow cytometry data from 44 healthy donors, 21 patients with chronic lymphocytic leukemia (CLL), and 15 patients with follicular lymphoma (FL). Approximately 15% of data from each group were used to build the profiles. Our approach was able to successfully identify 36/37 healthy donor cases, 18/18 CLL cases, and 12/13 FL cases., Conclusions: This proof-of-concept study demonstrated that an automated diagnosis of CLL and FL can be obtained by examining the cell capture rates of a test case using the computational method based on the multi-profile detection algorithm. The testing phase of our system is efficient and can facilitate diagnosis of B-lymphocyte neoplasms.

The goal of this study was to evaluate routine flow cytometric (FC) immunophenotypic markers in differentiating between Burkitt lymphoma (BL) and CD10+ diffuse large B-cell lymphoma (DLBCL). We performed retrospective analysis of FC data from 55 patients. We evaluated 9 FC parameters: forward and side scatter (FSC and SSC); mean fluorescent intensity (MFI) for CD20, CD10, CD38, CD79b, CD43, and CD71; and the percentage of neoplastic cells positive for CD71 (%CD71). The FSC; MFIs of CD10, CD43, CD79b, and CD71; and %CD71 cells were significantly different between BL and CD10+ DLBCL (P < .05; Student t test). A 5-point scoring system (FSC, %CD71, and MFIs of CD43, CD79b, and CD71) was devised, and 6 (60%) of 10 BLs scored 3 or greater and 1 (10%) of 10 CD10+ DLBCLs scored 3 (P = .04; χ(2)). Our findings indicate that routine FC parameters can aid in differentiating BL from CD10+ DLBCL.

Hepatocellular carcinoma (HCC) is the third leading cause of death due to cancer worldwide with over 500,000 people affected annually. Although chemotherapy has been widely used to treat patients with HCC, alternate modalities to specifically deliver therapeutic cargos to cancer cells have been sought in recent years due to the severe side effects of chemotherapy. In this respect, aptamer-based tumor targeted drug delivery has emerged as a promising approach to increase the efficacy of chemotherapy and reduce or eliminate drug toxicity. In this study, we developed a new HepG2-specific aptamer (HCA#3) by a procedure known as systematic evolution of ligands by exponential enrichment (SELEX) and exploited its role as a targeting ligand to deliver doxorubicin (Dox) to HepG2 cells in vitro. The selected 76-base nucleotide aptamer preferentially bound to HepG2 hepatocellular carcinoma cells but not to control cells. The aptamer HCA#3 was modified with paired CG repeats at the 5′-end to carry and deliver a high payload of intercalated Dox molecules at the CG sites. Four Dox molecules (mol/mol) were fully intercalated in each conjugate aptamer-Dox (ApDC) molecule. Biostability analysis showed that the ApDC molecules are stable in serum. Functional analysis showed that ApDC specifically targeted and released Dox within HepG2 cells but not in control cells, and treatment with HCA#3 ApDC induced HepG2 cell apoptosis but had minimal effect on control cells. Our study demonstrated that HCA#3 ApDC is a promising aptamer-targeted therapeutic that can specifically deliver and release a high doxorubicin payload in HCC cells. [ABSTRACT FROM AUTHOR]

Introduction: Manual gating of flow cytometry (FCM) data for marrow cell analysis is a standard approach in current practice, although it is time- and labor-consuming. Recent advances in cytometry technology have led to significant efforts in developing partially or fully automated analysis methods. Although multiple supervised and unsupervised FCM data analysis algorithms have been developed, they have not been widely adopted by the clinical and research laboratories. In this study, we evaluated flowDensity, an open source freely available algorithm, as an automated analysis tool for classification of lymphocyte subsets in the bone marrow biopsy specimens. Materials and Methods: FlowDensity-based gating was applied to 102 normal bone marrow samples and compared with the manual analysis. Independent expression of each cell marker was assessed for comprehensive expression analysis and visualization. Results: Our findings showed a correlation between the manual and flowDensity-based gating in the lymphocyte subsets. However, flowDensity-based gating in the populations with a small number of cells in each cluster showed a low degree of correlation. Comprehensive expression analysis successfully identified and visualized the lymphocyte subsets. Discussion: Our study found that although flowDensity might be a promising method for FCM data analysis, more optimization is required before implementing this algorithm into day-to-day workflow. [ABSTRACT FROM AUTHOR]

Anaplastic large cell lymphoma (ALCL) is the most common T-cell lymphoma in children. ALCL cells characteristically express surface CD30 molecules and carry the pathogenic ALK oncogene, both of which are diagnostic biomarkers and are also potential therapeutic targets. For precision therapy, we report herein a protamine nanomedicine incorporated with oligonucleotide aptamers to selectively target lymphoma cells, a dsDNA/drug payload to efficiently kill targeted cells, and an siRNA to specifically silence ALK oncogenes. The aptamer-equipped protamine nanomedicine was simply fabricated through a non-covalent charge-force reaction. The products had uniform structure morphology under an electron microscope and a peak diameter of 103 nm by dynamic light scattering measurement. Additionally, flow cytometry analysis demonstrated that under CD30 aptamer guidance, the protamine nanomedicine specifically bound to lymphoma cells, but did not react to off-target cells in control experiments. Moreover, specific cell targeting and intracellular delivery of the nanomedicine were also validated by electron and confocal microscopy. Finally, functional studies demonstrated that, through combined cell-selective chemotherapy using a drug payload and oncogene-specific gene therapy using an siRNA, the protamine nanomedicine effectively killed lymphoma cells with little toxicity to off-target cells, indicating its potential for precision therapy. [ABSTRACT FROM AUTHOR]