Development and characterization of on-chip biopolymer membranes.

In lab-on-a-chip applications, filtration is currently performed prior to sample loading or through pre-cast membranes adhered to the substrate. These membranes cannot be patterned to micrometer resolution, and their adhesion may be incompatible with the fabrication process or may introduce contaminants. We have developed an on-chip separation process using a biocompatible polymer that can be patterned and has controllable molecular rejection properties. We spun cast cellulose acetate (CA) membranes directly onto silicon wafers. Characterization of the molecular flux across the membrane showed that molecular weight and charge are major factors contributing to the membranes' rejection characteristics. Altering casting conditions such as polymer concentration in the casting solution and the quenching-bath composition and/or temperature allowed control of the molecular weight cut-off (MWCO). Three MWCOs; 300, 350, and 700 Da have been achieved for non-linear molecules. Molecular shape is also very important as much higher molecular weight single-stranded DNA was electrophoresed across the membranes while heme with a similar negative charge density was rejected. This was due to DNA's small molecular cross section. This is an important result because heme inhibits polymerase chain reactions (PCR) reducing the detection and characterization of DNA from blood samples.

Development of a rare cell fractionation device: application for cancer detection.

Isolating rare cells from biological fluids including whole blood or bone marrow is an interesting biological problem. Characterization of a few metastatic cells from cancer patients for further study is desirable for prognosis/diagnosis. Traditional methods have not proven adequate, due to the compositional complexity of blood, with its large numbers of cell types. To separate individual cells based on their mechanical characteristics, we have developed a series of massively parallel microfabricated sieving device. These devices were constructed with four successively narrower regions of channels numbering approximately 1800 per region. As cells traversed the device, they encountered each region and stopped at a gap width that prohibited passage due to their size. Cultured neuroblastoma cells, when mixed with whole blood and applied to the device, were retained in the 10-microm-wide by 20-microm-deep channels. All other cells migrated to the output. A derivative of the same device was utilized to characterize migration of whole blood. Adult white blood cells were retained at the 2.5-microm-wide by 5-microm-deep channels, while red blood cells passed through these channels. Devices designed to capture rare cells in peripheral circulation for downstream analysis will provide an important tool for diagnosis and treatment.

Automated three-dimensional tracing of neurons in confocal and brightfield images.

Automated three-dimensional (3-D) image analysis methods are presented for tracing of dye-injected neurons imaged by fluorescence confocal microscopy and HRP-stained neurons imaged by transmitted-light brightfield microscopy. An improved algorithm for adaptive 3-D skeletonization of noisy images enables the tracing. This algorithm operates by performing connectivity testing over large N x N x N voxel neighborhoods exploiting the sparseness of the structures of interest, robust surface detection that improves upon classical vacant neighbor schemes, improved handling of process ends or tips based on shape collapse prevention, and thickness-adaptive thinning. The confocal image stacks were skeletonized directly. The brightfield stacks required 3-D deconvolution. The results of skeletonization were analyzed to extract a graph representation. Topological and metric analyses can be carried out using this representation. A semiautomatic method was developed for reconnection of dendritic fragments that are disconnected due to insufficient dye penetration, an imaging deficiency, or skeletonization errors.

Median-based robust algorithms for tracing neurons from noisy confocal microscope images.

This paper presents a method to exploit rank statistics to improve fully automatic tracing of neurons from noisy digital confocal microscope images. Previously proposed exploratory tracing (vectorization) algorithms work by recursively following the neuronal topology, guided by responses of multiple directional correlation kernels. These algorithms were found to fail when the data was of lower quality (noisier, less contrast, weak signal, or more discontinuous structures). This type of data is commonly encountered in the study of neuronal growth on microfabricated surfaces. We show that by partitioning the correlation kernels in the tracing algorithm into multiple subkernels, and using the median of their responses as the guiding criterion improves the tracing precision from 41% to 89% for low-quality data, with a 5% improvement in recall. Improved handling was observed for artifacts such as discontinuities and/or hollowness of structures. The new algorithms require slightly higher amounts of computation, but are still acceptably fast, typically consuming less than 2 seconds on a personal computer (Pentium III, 500 MHz, 128 MB). They produce labeling for all somas present in the field, and a graph-theoretic representation of all dendritic/axonal structures that can be edited. Topological and size measurements such as area, length, and tortuosity are derived readily. The efficiency, accuracy, and fully-automated nature of the proposed method makes it attractive for large-scale applications such as high-throughput assays in the pharmaceutical industry, and study of neuron growth on nano/micro-fabricated structures. A careful quantitative validation of the proposed algorithms is provided against manually derived tracing, using a performance measure that combines the precision and recall metrics.

Rapid automated three-dimensional tracing of neurons from confocal image stacks.

Algorithms are presented for fully automatic three-dimensional (3-D) tracing of neurons that are imaged by fluorescence confocal microscopy. Unlike previous voxel-based skeletonization methods, the present approach works by recursively following the neuronal topology, using a set of 4 x N2 directional kernels (e.g., N = 32), guided by a generalized 3-D cylinder model. This method extends our prior work on exploratory tracing of retinal vasculature to 3-D space. Since the centerlines are of primary interest, the 3-D extension can be accomplished by four rather than six sets of kernels. Additional modifications, such as dynamic adaptation of the correlation kernels, and adaptive step size estimation, were introduced for achieving robustness to photon noise, varying contrast, and apparent discontinuity and/or hollowness of structures. The end product is a labeling of all somas present, graph-theoretic representations of all dendritic/axonal structures, and image statistics such as soma volume and centroid, soma interconnectivity, the longest branch, and lengths of all graph branches originating from a soma. This method is able to work directly with unprocessed confocal images, without expensive deconvolution or other preprocessing. It is much faster that skeletonization, typically consuming less than a minute to trace a 70-MB image on a 500-MHz computer. These properties make it attractive for large-scale automated tissue studies that require rapid on-line image analysis, such as high-throughput neurobiology/angiogenesis assays, and initiatives such as the Human Brain Project.