Estimation of Injection Volume in Capillary Microinjection Using Electrical Impedance Measurement
Hirvonen, Juha (2010)
Hirvonen, Juha
2010
Automaatiotekniikan koulutusohjelma
Automaatio-, kone- ja materiaalitekniikan tiedekunta
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Hyväksymispäivämäärä
2010-02-03
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201002261077
https://urn.fi/URN:NBN:fi:tty-201002261077
Tiivistelmä
Capillary pressure microinjection (CPM) is a tool for transporting small sample volumes into living cells utilizing a sharp glass pipette and pressure pulses. The automation level of the current state-of-the-art microinjection devices is low and this makes the technique slow, imprecise and inefficient. The objective of this thesis work is to develop a method to estimate the injection volume in the capillary pressure microinjection technique of living adherent cells. This method would improve the reliability and repeatability of CPM and facilitate automating the injection procedure.
Due to the extremely small dimensions involved in the process, a straight measurement of the injection volume is not possible. The strategy used in this work is to generate a mathematical model for the injection volume as a function of the injection pressure and the pipette electrical resistance. A measurement setup is built around a microinjection system to gather data for constructing the model. The injection pressure is measured with a pressure sensor, the pipette electrical resistance is determined using a custom-made impedance measurement circuitry and the injection volume is estimated by using a fluorescent dye as the injection liquid and recording image data from the injections. Several injection pressures and micropipette sizes are used to achieve data extensively enough. A MATLAB based automated algorithm is generated to handle the measurement data and organize the results efficiently.
The measurement results give a rough estimate of the relationship between the injection volume, the injection pressure and the pipette electrical resistance. However, a reliable model cannot be built based on the data. The reason is the rather limited amount of suitable measurement data for modelling it was possible to collect due to the numerous error situations. Nevertheless, new important information of the nature of the microinjection procedure is obtained and valuable observations on measurements connected to microinjection are made. Further studies must be done to solve the problems in the tests to be able to gather the data more efficiently and construct the actual model. /Kir10
Due to the extremely small dimensions involved in the process, a straight measurement of the injection volume is not possible. The strategy used in this work is to generate a mathematical model for the injection volume as a function of the injection pressure and the pipette electrical resistance. A measurement setup is built around a microinjection system to gather data for constructing the model. The injection pressure is measured with a pressure sensor, the pipette electrical resistance is determined using a custom-made impedance measurement circuitry and the injection volume is estimated by using a fluorescent dye as the injection liquid and recording image data from the injections. Several injection pressures and micropipette sizes are used to achieve data extensively enough. A MATLAB based automated algorithm is generated to handle the measurement data and organize the results efficiently.
The measurement results give a rough estimate of the relationship between the injection volume, the injection pressure and the pipette electrical resistance. However, a reliable model cannot be built based on the data. The reason is the rather limited amount of suitable measurement data for modelling it was possible to collect due to the numerous error situations. Nevertheless, new important information of the nature of the microinjection procedure is obtained and valuable observations on measurements connected to microinjection are made. Further studies must be done to solve the problems in the tests to be able to gather the data more efficiently and construct the actual model. /Kir10