Automated flow cytometric analysis across large numbers of samples and cell types.

Multi-parametric flow cytometry is a key technology for characterization of immune cell phenotypes. However, robust high-dimensional post-analytic strategies for automated data analysis in large numbers of donors are still lacking. Here, we report a computational pipeline, called FlowGM, which minimizes operator input, is insensitive to compensation settings, and can be adapted to different analytic panels. A Gaussian Mixture Model (GMM)-based approach was utilized for initial clustering, with the number of clusters determined using Bayesian Information Criterion. Meta-clustering in a reference donor permitted automated identification of 24 cell types across four panels. Cluster labels were integrated into FCS files, thus permitting comparisons to manual gating. Cell numbers and coefficient of variation (CV) were similar between FlowGM and conventional gating for lymphocyte populations, but notably FlowGM provided improved discrimination of "hard-to-gate" monocyte and dendritic cell (DC) subsets. FlowGM thus provides rapid high-dimensional analysis of cell phenotypes and is amenable to cohort studies.

Real-time label-free detection of dividing cells by means of lensfree video-microscopy.

Quantification of cell proliferation and monitoring its kinetics are essential in fields of research such as developmental biology, oncology, etc. Although several proliferation assays exist, monitoring cell proliferation kinetics remains challenging. We present a novel cell proliferation assay based on real-time monitoring of cell culture inside a standard incubator using a lensfree video-microscope, combined with automated detection of single cell divisions over a population of several thousand cells. Since the method is based on direct visualization of dividing cells, it is label-free, continuous, and not sample destructive. Kinetics of cell proliferation can be monitored from a few hours to several days. We compare our method to a standard assay, the EdU proliferation assay, and as proof of principle, we demonstrate concentration-dependent and time-dependent effect of actinomycin D-a cell proliferation inhibitor.

High-resolution 3-D imaging of living cells in suspension using confocal axial tomography.

Conventional flow cytometry (FC) methods report optical signals integrated from individual cells at throughput rates as high as thousands of cells per second. This is further combined with the powerful utility to subsequently sort and/or recover the cells of interest. However, these methods cannot extract spatial information. This limitation has prompted efforts by some commercial manufacturers to produce state-of-the-art commercial flow cytometry systems allowing fluorescence images to be recorded by an imaging detector. Nonetheless, there remains an immediate and growing need for technologies facilitating spatial analysis of fluorescent signals from cells maintained in flow suspension. Here, we report a novel methodological approach to this problem that combines micro-fluidic flow, and microelectrode dielectric-field control to manipulate, immobilize and image individual cells in suspension. The method also offers unique possibilities for imaging studies on cells in suspension. In particular, we report the system's immediate utility for confocal "axial tomography" using micro-rotation imaging and show that it greatly enhances 3-D optical resolution compared with conventional light reconstruction (deconvolution) image data treatment. That the method we present here is relatively rapid and lends itself to full automation suggests its eventual utility for 3-D imaging cytometry.

Using hidden scale for salient object detection.

This paper describes a method for detecting salient regions in remote-sensed images, based on scale and contrast interaction. We consider the focus on salient structures as the first stage of an object detection/recognition algorithm, where the salient regions are those likely to contain objects of interest. Salient objects are modeled as spatially localized and contrasted structures with any kind of shape or size. Their detection exploits a probabilistic mixture model that takes two series of multiscale features as input, one that is more sensitive to contrast information, and one that is able to select scale. The model combines them to classify each pixel in salient/nonsalient class, giving a binary segmentation of the image. The few parameters are learned with an EM-type algorithm.