Optical method for automated measurement of glass micropipette tip geometry.
Many experimental biological techniques utilize hollow glass needles called micropipettes to perform fluid extraction, cell manipulation, and electrophysiological recordings For electrophysiological recordings, micropipettes are typically fabricated immediately before use using a "pipette puller", which uses open-loop control to heat a hollow glass capillary while applying a tensile load. Variability between manufactured micropipettes requires a highly trained operator to qualitatively inspect each micropipette; typically this is achieved by viewing the pipette under 40-100x magnification in order to ensure that the tip has the correct shape (e.g., outer diameter, cone angle, taper length). Since laboratories may use hundreds of micropipettes per week, significant time demands are associated with micropipette inspection. Here, we have automated the measurement of micropipette tip outer diameter and cone angle using optical microscopy. The process features repeatable constraint of the micropipette, quickly and automatically moving the micropipette to bring its tip into the field of view, focusing on the tip, and computing tip outer diameter and cone angle measurements from the acquired images by applying a series of image processing algorithms. As implemented on a custom automated microscope, these methods achieved, with 95% confidence, ±0.38 µm repeatability in outer diameter measurement and ±5.45° repeatability in cone angle measurement, comparable to a trained human operator. Accuracy was evaluated by comparing optical pipette measurements with measurements obtained using scanning electron microscopy (SEM); optical outer diameter measurements differed from SEM by 0.35 ± 0.36 µm and optical cone angle measurements differed from SEM by -0.23 ± 2.32°. The algorithms we developed are adaptable to most commercial automated microscopes and provide a skill-free route to rapid, quantitative measurement of pipette tip geometry with high resolution, accuracy, and repeatability. Further, these methods are an important step toward a closed-loop, fully-automated micropipette fabrication system.
G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332.
Max A
MA
Stockslager
G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332.
Christopher M
CM
Capocasale
G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332.
Gregory L
GL
Holst
G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332.
Michael D
MD
Simon
G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332.
Yuanda
Y
Li
G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332.
Dustin J
DJ
McGruder
SUNY Polytechnic Institute, Colleges of Nanoscale Science and Engineering, 257 Fuller Rd, Albany, NY 12203.
Erin B
EB
Rousseau
W.H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332.
William A
WA
Stoy
G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332.
Todd
T
Sulchek
G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332.
Craig R
CR
Forest
image processing; micropipette; microscope
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