How to Prevent yourself from Seeing Double

A Flow Cytometry-Based Examination of the Mouse White Blood Cell Differential in the Context of Age and Sex

Analysis of the white blood cell differential as part of a flow cytometry-based approach is a common routine diagnostic tool used in clinics and research. For human blood, the methodological approach, suitable markers, and gating strategies are well-established. However, there is a lack of information regarding the mouse blood count. In this article, we deliver a fast and easy protocol for reprocessing mouse blood for the purpose of flow cytometric analysis, as well as suitable markers and gating strategies. We also present two possible applications: for the analysis of the whole blood count, with blood from a cardiac puncture, and for the analysis of a certain leukocyte subset at multiple time points in the framework of a mouse experiment, using blood from the facial vein. Additionally, we provide orientation values by applying the method to 3-month-old and 24-month-old male and female C57BL/6J mice. Our analyses demonstrate differences in the leukocyte fractions depending on age and sex. We discuss the influencing factors and limitations that can affect the results and that, therefore, need to be considered when applying this method. The present study fills the gap in the knowledge related to the rare information on flow cytometric analysis of mouse blood and, thus, lays the foundation for further investigations in this area.

Reproducibility in cytometry: Signals analysis and its connection to uncertainty quantification

Signals analysis for cytometry remains a challenging task that has a significant impact on uncertainty. Conventional cytometers assume that individual measurements are well characterized by simple properties such as the signal area, width, and height. However, these approaches have difficulty distinguishing inherent biological variability from instrument artifacts and operating conditions. As a result, it is challenging to quantify uncertainty in the properties of individual cells and perform tasks such as doublet deconvolution. We address these problems via signals analysis techniques that use scale transformations to: (I) separate variation in biomarker expression from effects due to flow conditions and particle size; (II) quantify reproducibility associated with a given laser interrogation region; (III) estimate uncertainty in measurement values on a per-event basis; and (IV) extract the singlets that make up a multiplet. The key idea behind this approach is to model how variable operating conditions deform the signal shape and then use constrained optimization to "undo" these deformations for measured signals; residuals to this process characterize reproducibility. Using a recently developed microfluidic cytometer, we demonstrate that these techniques can account for instrument and measurand induced variability with a residual uncertainty of less than 2.5% in the signal shape and less than 1% in integrated area.

Flow Cytometry and Mass Cytometry for Measuring the Immune Cell Infiltrate in Atherosclerotic Arteries

Atherosclerosis is characterized by the abundant infiltration of immune cells starting at early stages and progressing to late stages of the disease. The study and characterization of immune cells infiltrating and residing in the aorta has being tackled by several methodologies such as flow cytometry and mass cytometry (CyTOF). Flow cytometry has been primarily used to address the aortic leukocyte composition; however, only a limited number of markers can be analyzed simultaneously. CyTOF started to overcome these limitations by employing rare element-tagged antibodies and combines mass spectrometry with the ease and precision of flow cytometry. CyTOF currently allows for the simultaneous measurement of more than 40 cellular parameters at single-cell resolution.In this chapter, we describe the methodology used to isolate single immune cells from mouse aortas, followed by protocols for flow cytometry and CyTOF for aortic immune cell characterization.

Mucin-producing hamster cholangiocarcinoma cell line, Ham-2, possesses the aggressive cancer phenotypes with liver and lung metastases

Cholangiocarcinoma (CCA) is an aggressive bile duct cancer. Opisthorchis viverrini (O. viverrini) infection is a significant cause of CCA in the Greater Mekong subregion. Currently, there is no standard chemotherapeutic regimen for CCA. A unique hamster carcinogenesis model of O. viverrini-associated CCA was established. Molecular targets identified from the hamster CCA-comparative model are valuable for target identification and validation. Hamster CCA was induced by the administration of O. viverrini metacercariae and N-nitrosodimethylamine. Hamster-derived cancer cells were isolated and continuously cultured for more than 6 months. Ham-2 cell line was established and characterized in vitro and in vivo. Ham-2 exhibited chromosome hyperploidy. A comparative study with previously established cell line, Ham-1, demonstrated that Ham-2 acquired slower growth, higher adhesion, higher migration, and resistance to doxorubicin and 5-fluorouracil (5-FU). In BALB/c Rag-2/Jak3 double-deficient (BRJ) mice, Ham-2 subcutaneous transplantation formed mucin-producing cancers, which morphologically resemble human tubular cholangiocarcinoma. Intravenous-injected Ham-2 established the metastatic nodules in the lungs and livers of BRJ mice. Altogether, a new hamster cholangiocarcinoma cell line, Ham-2, which acquired more aggressive phenotypes in vitro and in vivo, was established. This cell line might be a valuable tool for comparative drug target identification and validation.

Applications of flow cytometry sorting in the pharmaceutical industry: A review

The article reviews applications of flow cytometry sorting in manufacturing of pharmaceuticals. Flow cytometry sorting is an extremely powerful tool for monitoring, screening and separating single cells based on any property that can be measured by flow cytometry. Different applications of flow cytometry sorting are classified into groups and discussed in separate sections as follows: (a) isolation of cell types, (b) high throughput screening, (c) cell surface display, (d) droplet fluorescent-activated cell sorting (FACS). Future opportunities are identified including: (a) sorting of particular fractions of the cell population based on a property of interest for generating inoculum that will result in improved outcomes of cell cultures and (b) the use of population balance models in combination with FACS to design and optimize cell cultures.