Engineering Novel Lab Devices Using 3D Printing and Microcontrollers.

The application of 3D printing and microcontrollers allows users to rapidly engineer novel hardware solutions useful in a laboratory environment. 3D printing is transformative as it enables the rapid fabrication of adapters, housings, jigs, and small structural elements. Microcontrollers allow for the creation of simple, inexpensive machines that receive input from one or more sensors to trigger a mechanical or electrical output. Bringing these technologies together, we have developed custom solutions that improve capabilities and reduce costs, errors, and human intervention. In this article, we describe three devices: JetLid, TipWaster, and Remote Monitoring Device (REMIND). JetLid employs a microcontroller and presence sensor to trigger a high-speed fan that reliably de-lids microtiter plates on a high-throughput screening system. TipWaster uses a presence sensor to activate an active tip waste chute when tips are ejected from a pipetting head. REMIND is a wireless, networked lab monitoring device. In its current implementation, it monitors the liquid level of waste collection vessels or bulk liquid reagent containers. The modularity of this device makes adaptation to other sensors (temperature, humidity, light/darkness, movement, etc.) relatively simple. These three devices illustrate how 3D printing and microcontrollers have enabled the process of rapidly turning ideas into useful devices.

Reproducibility of gene expression signature-based predictions in replicate experiments.

PURPOSE: The goals of this analysis were to (a) determine concordance of gene expression results from replicate experiments, (b) examine prediction agreement of multigene predictors on replicate data, and (c) assess the robustness of prediction results in the face of noise. PATIENTS AND METHODS: Affymetrix U133A gene chips were used for gene expression profiling of 97 fine-needle aspiration biopsies from breast cancer. Thirty-five cases were profiled in replicates: 17 within the same laboratory, 11 in two different laboratories, and 15 to assess manual and robotic labeling. We used data from 62 cases to develop 111 distinct pharmacogenomic predictors of response to therapy. These were tested on cases profiled in duplicates to determine prediction agreement and accuracy. To evaluate the robustness of the pharmacogenomic predictors, we also introduced random noise into the informative genes in one half of the replicates. RESULTS: The average concordance correlation coefficient was 0.978 (range, 0.96-0.99) for intralaboratory replicates, 0.962 (range, 0.94-0.98) for between-laboratory replicates, and 0.971 (range, 0.93-0.99) for manual versus robotic labeling. The mean % prediction agreement on replicate data was 97% (95% CI, 0.96-0.98; SD, 0.006), 92% (95% CI, 0.90-0.93; SD, 0.009), and 94% (95% CI, 0.92-0.95; SD, 0.008) for support vector machines, diagonal linear discriminant analysis, and k-nearest neighbor prediction methods, respectively. Mean accuracy in the test set was 77% (95% CI, 0.74-0.79; SD, 0.014), 66% (95% CI, 0.63-0.73; SD, 0.015), and 64% (95% CI, 0.60-0.67; SD, 0.016), respectively. CONCLUSION: Gene expression results obtained with Affymetrix U133A chips are highly reproducible within and across two high-volume laboratories. Pharmacogenomic predictions yielded >90% agreement in replicate data.