We present a robotic manipulation system for automated selection and transfer of individual living cells to analysis locations. We begin with a commonly used cell transfer technique using glass capillary micropipettes to aspirate and release living cells suspended in liquid growth media. Using vision-based feedback and closed-loop process control, two individual three-axis robotic stages position the micropipette tip in proximity to the cell of interest. The cell is aspirated and the tip is moved to a target location where the cell is dispensed. Computer vision is used to monitor and inspect the success of the dispensing process. In our initial application, the target cell destination is a microwell etched in a fused silica substrate. The system offers a robust and flexible technology for cell selection and manipulation. Applications for this technology include embryonic stem cells transfer, blastomere biopsy, cell patterning, and cell surgery.

MOTIVATION: Gene expression profiling technologies can generally produce mRNA abundance data for all genes in a genome. A dearth of proteomic data persists because identification range and sensitivity of proteomic measurements lag behind those of transcriptomic measurements. Using partial proteomic data, it is likely that integrative transcriptomic and proteomic analysis may introduce significant bias. Developing methodologies to accurately estimate missing proteomic data will allow better integration of transcriptomic and proteomic datasets and provide deeper insight into metabolic mechanisms underlying complex biological systems. RESULTS: In this study, we present a non-linear data-driven model to predict abundance for undetected proteins using two independent datasets of cognate transcriptomic and proteomic data collected from Desulfovibrio vulgaris. We use stochastic gradient boosted trees (GBT) to uncover possible non-linear relationships between transcriptomic and proteomic data, and to predict protein abundance for the proteins not experimentally detected based on relevant predictors such as mRNA abundance, cellular role, molecular weight, sequence length, protein length, guanine-cytosine (GC) content and triple codon counts. Initially, we constructed a GBT model using all possible variables to assess their relative importance and characterize the behavior of the predictive model. A strong plateau effect in the regions of high mRNA values and sparse data occurred in this model. Hence, we removed genes in those areas based on thresholds estimated from the partial dependency plots where this behavior was captured. At this stage, only the strongest predictors of protein abundance were retained to reduce the complexity of the GBT model. After removing genes in the plateau region, mRNA abundance, main cellular functional categories and few triple codon counts emerged as the top-ranked predictors of protein abundance. We then created a new tuned GBT model using the five most significant predictors. The construction of our non-linear model consists of a set of serial regression trees models with implicit strength in variable selection. The model provides variable relative importance measures using as a criterion mean square error. The results showed that coefficients of determination for our nonlinear models ranged from 0.393 to 0.582 in both datasets, providing better results than linear regression used in the past. We evaluated the validity of this non-linear model using biological information of operons, regulons and pathways, and the results demonstrated that the coefficients of variation of estimated protein abundance values within operons, regulons or pathways are indeed smaller than those for random groups of proteins. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The central theme in this special section is recent progress in automation for drug delivery. There are five papers in this section.

A novel system that has enabled the measurement of single-cell oxygen consumption rates is presented. The experimental apparatus includes a temperature controlled environmental chamber, an array of microwells etched in glass, and a lid actuator used to seal cells in the microwells. Each microwell contains an oxygen sensitive platinum phosphor sensor used to monitor the cellular metabolic rates. Custom automation software controls the digital image data collection for oxygen sensor measurements, which are analyzed using an image-processing program to yield the oxygen concentration within each microwell versus time. Two proof-of-concept experiments produced oxygen consumption rate measurements for A549 human epithelial lung cancer cells of 5.39 and 5.27 fmol/min/cell, closely matching published oxygen consumption rates for bulk A549 populations.

In order to improve the steric hindrances and less ordered structures in the long-chain SAMs, the SAM of 4,4-Dithiodibutyric acid (4,4- DTBA) was investigated to be a thin monolayer for the protein chip. This work presents the feasibility study of 4,4- DTBA as a monolayer of the protein chip based on gold surface. Characterizations of surface topography using atomic force microscopy (AFM), contact angle and fluorescence using protein A-FITC were performed to investigate the binding efficiency for the SAMs of 4,4- DTBA and 11- mercaptoundecanoic acid (11- MUA). The results show that the binding efficiency of 4,4- DTBA is higher than that of 11-MUA. The SAM of 4,4-DTBA can be used as a monolayer for the protein chip and may have a comparable binding efficiency.

This paper describes the development of a real-time polymerase chain reaction testbed for the analysis of up to 48 1- to 2-/spl mu/L reactions in glass capillaries. It is suitable for high-throughput experimentation when used as a downstream module for ACAPELLA-5K, an automated, high-precision, fluid-handling system developed in our laboratory over the past five years. The testbed features a laser-induced fluorescence scanner with high-sensitivity photomultiplier tubes for the detection of three spectral wavelengths. The scanner has a detection threshold of 1-nM fluorescein in a 1-/spl mu/L detection volume. Peltier-effect thermoelectric elements are used to control thermal cycles with a slew rate of 3/spl deg/C/s, and a set point tracking accuracy of /spl plusmn/0.5/spl deg/C. Analyzer design and performance are presented. Experimental results show a successful detection of 3 copies//spl mu/L of the albumin gene in serial dilutions of human genomic deoxyribonucleic-acid. Detection levels down to 4 copies//spl mu/L of the t(14;18) translocation associated with the cancer follicular lymphoma, was demonstrated in RL-7 cells. Note to Practitioners-Real-time polymerase chain reaction is a thermally driven in-vitro replication of specific segments of DNA, with simultaneous monitoring or reaction product by means of fluorescence detection. This process is important in biochemical analysis for the detection and/or estimation of gene concentration in DNA samples. By increasing the concentration of an initially undetectable gene and monitoring the kinetic history of the reaction, an estimate of the initial concentration can be computed. Although there exists a plethora of commercial real-time PCR instruments, none of these instruments are suitable for low-volume (single-digit microliters) reagents and high-throughput processing while preserving a high degree of fidelity to obtain reliable, repeatable results, and versatility (support for different fluorogenic probes, different operating modes, etc.). The A5K-automated fluid handling and sample preparation system addresses the high-throughput reaction preparation in small-volume glass capillaries. The testbed presented here is a downstream processing module for A5K. To address the challenges posed by the use of low-volume, elongated reaction vessels, we employ a scanning optical system with a novel inline illumination method to address up to 48 capillaries. We empirically demonstrate that the system's performance is comparable to that of existing commercial real-time PCR platforms, and verify its repeatability and versatility. Future work will address the development of a more compact instrument that can be directly integrated with A5K.

Yeast pedigree analysis-isolation and characterization of the products of mitotic cell divisions throughout the lifespan of an individual cell-is a manually intensive process that requires a biologist to manipulate single yeast cells every 90 minutes for as long as 150 hours. Progress toward the development of a system for automating yeast pedigree analysis is presented. Yeast cells are trapped in a microfluidic device, then observed using a fiber-optic imaging bundle. Image processing software classifies each cell as either budding or not budding a newly formed daughter cell. The system recognizes when a cell has a bud, and microfluidic valves direct newly formed daughter cells to an agar plate. Finally, biologists analyze the colonies formed by each yeast daughter cell on the agar plate.

A capillary-based fluid handling system bas been developed to process 5000 samples in 8 hours. The system takes deoxyribonucleic acid (DNA) or other chemical samples and automatically processes them as specified in a user-defined protocol with aspiration, dispensing, mixing, thermal cycling, and imaging steps. A serial pipeline process is used to provide flexibility, reproducibility, and reliability for the samples prepared. This laboratory automation system dispenses 40-100 pL droplet volumes using piezoelectric reagent dispensers and prepares 1 or 2-/spl mu/L final reaction volumes, a 3 to 5-fold reduction in reagent usage over current (2003) state-of-the-art manual and automated instrumentation. Extensive testing of the system has been performed with the University of Washington Genome Center. Applications for ACAPELLA-5K include DNA sequencing, diagnostics, minimal residual disease quantification, drug discovery, environmental testing, forensics, protein crystallography, Polymerase Chain Reaction (PCR) and so on.

The Genomation Laboratory at the University of Washington has been developing ACAPELLA, an automated submicroliter fluid sample handling system. This general-purpose, chemistry analysis system has applications to DNA sequencing, diagnostics, forensics, protein crystallography, and so on. In the first phase of prototype development, the ACAPELLA-IK system was designed to process 1,000 samples in 8 hours. After extensive tests and subsystem modifications, this system has successfully demonstrated high-throughput preparation of restriction enzyme digests, polymerase chain reactions, and sequencing reactions for genome analysis. The lessons learned in this prototype development and testing phase have been used to design the next generation ACAPELLA-SK system that processes 5,000 samples in 8 hours and has more functionality. The prototype design, development, and evolution into the ACAPELLA-SK system are described, and the ACAPELLA-SK system is presented.

The Jacobian matrix, J, relates joint velocity to Cartesian velocity for an N-link serial manipulator. An operator factorization and inversion of /sup J/* /sup J/ is shown to result in an order (N) spatially recursive filtering and smoothing algorithm that solves the inverse Jacobian problems of finding the joint angle velocities given the end-effector velocity or finding the joint angle accelerations given the end-effector acceleration. It is shown that, with a proper model, these inverse Jacobian problems are equivalent to solving the forward dynamics problem for the same model. The recursive algorithm developed by G. Rodriguez (1987) to solve the forward dynamics problem is applied directly to solve these inverse Jacobian problems.>