Suhasa Bangalore Kodandaramaiah

20130225963, Aug 29, 2013

Nov 13, 2012

13/676082

Suhasa Bangalore Kodandaramaiah - Somerville MA, US
Edward Stuart Boyden - Chestnut Hill MA, US
Crag Richard Forest - Atlanta GA, US
Brian Yichiun Chow - Cambridge MA, US
Giovanni Talei Franzes - Boston MA, US

Georgia Tech Research Corporation - Atlanta GA
Massachusetts Institute of Technology - Cambridge MA

A61B 18/14

600373

In an automated methodology for carrying out in vivo cell patch clamping, a cell patch clamping device is automatically moved into position and targeted to a neuron. Neuron contact is determined by analyzing the temporal series of measured resistance levels at the cell patch clamping device as it is moved. The difference between successive resistance levels is computed and compared to a threshold, which must be exceeded for a minimum number of computations before neuron contact is assumed. Pneumatic control methods are used to achieve gigaseal formation and cell break-in, leading to whole-cell patch clamp formation. An automated robotic system capable of performing this methodology automatically performs patch clamping in vivo, automatically detecting cells by analyzing the temporal sequence of electrode impedance changes. By continuously monitoring the patching process and rapidly executing actions triggered by specific measurements, the robot can rapidly find neurons in the living brain and establish recordings.

20180028081, Feb 1, 2018

Jul 6, 2017

15/643462

- Cambridge MA, US
- Atlanta GA, US
Ingrid van Welie - Newton MA, US
Brian Douglas Allen - Cambridge MA, US
Suhasa B. Kodandaramaiah - Minneapolis MN, US
Craig R. Forest - Atlanta GA, US

Massachusetts Institute of Technology - Cambridge MA
Georgia Tech Research Corporation - Atlanta GA

A61B 5/04
A61B 5/00
A61B 34/32
A61B 5/053

In an automated methodology for in vivo image-guided cell patch clamping, a cell patch clamping device is moved into position and targeted to a specific cell using automated image-guided techniques. Cell contact is determined by analyzing the temporal series of measured resistance levels at the clamping device as it is moved. The difference between successive resistance levels is compared to a threshold, which must be exceeded before cell contact is assumed. Pneumatic control methods are used to achieve gigaseal formation and cell break-in, leading to whole-cell patch clamp formation. An automated robotic system capable of performing this methodology automatically performs patch clamping in vivo, automatically locating cells through image guidance and by analyzing the temporal sequence of electrode impedance changes. By continuously monitoring the patching process and rapidly executing actions triggered by specific measurements, the robot can rapidly find target cells in vivo and establish patch-clamp recordings.

20140228857, Aug 14, 2014

Nov 13, 2013

14/079630

Suhasa Bangalore Kodandaramaiah - Somerville MA, US
Edward Stuart Boyden - Chestnut Hill MA, US
Craig Richard Forest - Atlanta GA, US

GEORGIA TECH RESEARCH CORPORATION - Atlanta GA
MASSACHUSETTS INSTITUTE OF TECHNOLOGY - Cambridge MA

A61B 19/00

606130

In an automated method for in vivo multiple cell patch clamping, cell patch clamping devices are automatically moved into position and targeted to multiple corresponding cells. Cell contact is determined by analyzing the temporal series of measured resistance levels at the clamping devices as they are moved. The difference between successive resistance levels is computed and compared to a threshold, which must be exceeded for a minimum number of computations before neuron contact is assumed. Pneumatic control methods are used to achieve cell-attached or gigaseal formation and subsequent cell break-in, leading to whole-cell patch clamp formation. An automated robotic system automatically performs patch clamping in vivo, automatically detecting cells according to the methodology by analyzing the temporal sequence of electrode impedance changes. By continuously monitoring the patching process and rapidly executing actions triggered by specific measurements, the robot can rapidly find neurons in the living brain and establish recordings.