Inhibition of mitochondrial respiratory chain complex I by rotenone had been found to induce cell death in a variety of cells. However, the mechanism is still elusive. Because reactive oxygen species (ROS) play an important role in apoptosis and inhibition of mitochondrial respiratory chain complex I by rotenone was thought to be able to elevate mitochondrial ROS production, we investigated the relationship between rotenone-induced apoptosis and mitochondrial reactive oxygen species. Rotenone was able to induce mitochondrial complex I substrate-supported mitochondrial ROS production both in isolated mitochondria from HL-60 cells as well as in cultured cells. Rotenone-induced apoptosis was confirmed by DNA fragmentation, cytochrome c release, and caspase 3 activity. A quantitative correlation between rotenone-induced apoptosis and rotenone-induced mitochondrial ROS production was identified. Rotenone-induced apoptosis was inhibited by treatment with antioxidants (glutathione, N-acetylcysteine, and vitamin C). The role of rotenone-induced mitochondrial ROS in apoptosis was also confirmed by the finding that HT1080 cells overexpressing magnesium superoxide dismutase were more resistant to rotenone-induced apoptosis than control cells. These results suggest that rotenone is able to induce apoptosis via enhancing the amount of mitochondrial reactive oxygen species production.

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer‐reviewed by leading experts in the field, making this an essential research companion.

International audienceThe classical model of hematopoiesis established in the mouse postulates that lymphoid cells originate from a founder population of common lymphoid progenitors. Here, using a modeling approach in humanized mice, we showed that human lymphoid development stemmed from distinct populations of CD127(-) and CD127(+) early lymphoid progenitors (ELPs). Combining molecular analyses with in vitro and in vivo functional assays, we demonstrated that CD127(-) and CD127(+) ELPs emerged independently from lympho-mono-dendritic progenitors, responded differently to Notch1 signals, underwent divergent modes of lineage restriction, and displayed both common and specific differentiation potentials. Whereas CD127(-) ELPs comprised precursors of T cells, marginal zone B cells, and natural killer (NK) and innate lymphoid cells (ILCs), CD127(+) ELPs supported production of all NK cell, ILC, and B cell populations but lacked T potential. On the basis of these results, we propose a "two-family" model of human lymphoid development that differs from the prevailing model of hematopoiesis

The development of the next generation of biomaterials for restoration of tissues and organs (i.e., tissue engineering) requires a better understanding of the extracellular matrix (ECM) and its interaction with cells. Extracellular matrix is a macromolecular assembly of natural biopolymers including collagens, glycosaminoglycans (GAGs), proteoglycans (PGs), and glycoproteins. Interestingly, several ECM components have the ability to form three‐dimensional (3D), supramolecular matrices (scaffolds) in vitro by a process of self‐directed polymerization, “self‐assembly”. It has been shown previously that 3D matrices with distinct architectural and biological properties can be formed from either purified type I collagen or a complex mixture of interstitial ECM components derived from intestinal submucosa. Unfortunately, many of the imaging and analysis techniques available to study these matrices either are unable to provide insight into 3D preparations or demand efforts that are often prohibitory to observations of living, dynamic systems. This is the first report on the use of reflection imaging at rapid time intervals combined with laser‐scanning confocal microscopy for analysis of structural properties and kinetics of collagen and ECM assembly in 3D. We compared time‐lapse confocal reflection microscopy (TL‐CRM) with a well‐established spectrophotometric method for determining the self‐assembly properties of both purified type I collagen and soluble interstitial ECM. While both TL‐CRM and spectrophotometric techniques provided insight into the kinetics of the polymerization process, only TL‐CRM allowed qualitative and quantitative evaluation of the structural parameters (e.g., fibril diameter) and 3D organization (e.g., fibril density) of component fibrils over time. Matrices formed from the complex mixture of soluble interstitial ECM components showed an increased rate of assembly, decreased opacity, decreased fibril diameter, and increased fibril density compared to that of purified type I collagen. These results suggested that the PG/GAG components of soluble interstitial ECM were affecting the polymerization of the component collagens. Therefore, the effects of purified and complex mixtures of PG/GAG components on the assembly properties of type I collagen and interstitial ECM were evaluated. The data confirmed that the presence of PG/GAG components altered the kinetics and the 3D fibril morphology of assembled matrices. In summary, TL‐CRM was demonstrated to be a new and useful technique for analysis of the 3D assembly properties of collagen and other natural biopolymers which requires no specimen fixation and/or staining.© 2000 John Wiley & Sons, Inc. Biopoly 54: 222–234, 2000

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Flow cytometry is one of the most important technologies for high-throughput single-cell analysis. Fluorescent labeling acts as the primary approach for cellular analysis in flow cytometry. Nevertheless, the fluorescent tags are not applicable to all cases, especially to small molecules, for which labeling may significantly perturb the biological functionality. Spontaneous Raman scattering flow cytometry offers the capability to non-invasively detect chemical contents of cells but suffers from slow data acquisition. In order to achieve label-free high-throughput single-particle analysis using Raman scattering, we developed a 32-channel multiplex stimulated Raman scattering flow cytometry (SRS-FC) technique that can measure chemical contents of single particles at a speed of 5 μs per Raman spectrum. Using mixed polymer beads, we demonstrate the discrimination of different particles at a throughput of up to 11,000 particles per second. This is a four orders of magnitude improvement in throughput compared to conventional spontaneous Raman flow cytometry. As a proof of concept, we show the differentiation of 3T3-L1 cells at different states by SRS-FC according to the difference in cellular chemical content. The SRS-FC technique opens new opportunities for high-throughput and high-content chemical analysis of live cells in a label-free manner.

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Bacterial contamination by Listeria monocytogenes not only puts the public at risk, but also is costly for the food-processing industry. Traditional biochemical methods for pathogen identification require complicated sample preparation for reliable results. Optical scattering technology has been used for identification of bacterial cells in suspension, but with only limited success. Therefore, to improve the efficacy of the identification process using our novel imaging approach, we analyze bacterial colonies grown on solid surfaces. The work presented here demonstrates an application of computer-vision and pattern-recognition techniques to classify scatter patterns formed by Listeria colonies. Bacterial colonies are analyzed with a laser scatterometer. Features of circular scatter patterns formed by bacterial colonies illuminated by laser light are characterized using Zernike moment invariants. Principal component analysis and hierarchical clustering are performed on the results of feature extraction. Classification using linear discriminant analysis, partial least squares, and neural networks is capable of separating different strains of Listeria with a low error rate. The demonstrated system is also able to determine automatically the pathogenicity of bacteria on the basis of colony scatter patterns. We conclude that the obtained results are encouraging, and strongly suggest the feasibility of image-based biodetection systems.