Literature DB >> 27672230

Optical method for automated measurement of glass micropipette tip geometry.

Max A Stockslager1, Christopher M Capocasale1, Gregory L Holst1, Michael D Simon1, Yuanda Li1, Dustin J McGruder1, Erin B Rousseau2, William A Stoy3, Todd Sulchek1, Craig R Forest1.   

Abstract

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.

Entities:  

Keywords:  image processing; micropipette; microscope

Year:  2016        PMID: 27672230      PMCID: PMC5034878          DOI: 10.1016/j.precisioneng.2016.04.003

Source DB:  PubMed          Journal:  Precis Eng        ISSN: 0141-6359            Impact factor:   3.156


  7 in total

1.  Fluorescent pipettes for optically targeted patch-clamp recordings.

Authors:  Daisuke Ishikawa; Naoya Takahashi; Takuya Sasaki; Atsushi Usami; Norio Matsuki; Yuji Ikegaya
Journal:  Neural Netw       Date:  2010-02-24

2.  A computational approach to edge detection.

Authors:  J Canny
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  1986-06       Impact factor: 6.226

3.  Bubble pressure measurement of micropipet tip outer diameter.

Authors:  S Mittman; D G Flaming; D R Copenhagen; J H Belgum
Journal:  J Neurosci Methods       Date:  1987-12       Impact factor: 2.390

4.  A comparison of different focus functions for use in autofocus algorithms.

Authors:  F C Groen; I T Young; G Ligthart
Journal:  Cytometry       Date:  1985-03

5.  Pressure polishing: a method for re-shaping patch pipettes during fire polishing.

Authors:  M B Goodman; S R Lockery
Journal:  J Neurosci Methods       Date:  2000-07-31       Impact factor: 2.390

6.  Method for estimating the tip geometry of scanning ion conductance microscope pipets.

Authors:  Matthew Caldwell; Samantha J L Del Linz; Trevor G Smart; Guy W J Moss
Journal:  Anal Chem       Date:  2012-10-19       Impact factor: 6.986

7.  Automated whole-cell patch-clamp electrophysiology of neurons in vivo.

Authors:  Suhasa B Kodandaramaiah; Giovanni Talei Franzesi; Brian Y Chow; Edward S Boyden; Craig R Forest
Journal:  Nat Methods       Date:  2012-05-06       Impact factor: 28.547

  7 in total
  3 in total

1.  Autonomous patch-clamp robot for functional characterization of neurons in vivo: development and application to mouse visual cortex.

Authors:  Gregory L Holst; William Stoy; Bo Yang; Ilya Kolb; Suhasa B Kodandaramaiah; Lu Li; Ulf Knoblich; Hongkui Zeng; Bilal Haider; Edward S Boyden; Craig R Forest
Journal:  J Neurophysiol       Date:  2019-04-10       Impact factor: 2.714

2.  Cleaning patch-clamp pipettes for immediate reuse.

Authors:  I Kolb; W A Stoy; E B Rousseau; O A Moody; A Jenkins; C R Forest
Journal:  Sci Rep       Date:  2016-10-11       Impact factor: 4.379

3.  Multi-neuron intracellular recording in vivo via interacting autopatching robots.

Authors:  Suhasa B Kodandaramaiah; Francisco J Flores; Edward S Boyden; Craig R Forest; Gregory L Holst; Annabelle C Singer; Xue Han; Emery N Brown
Journal:  Elife       Date:  2018-01-03       Impact factor: 8.140

  3 in total

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