High Content Screening and Analysis

The cost of discovery for a new drug now exceeds $800 Million USD.

The process of High Throughput Screening (HTS) continues to drive the number of potential drug candidates. This accelerating trend will impact Drug Discovery well into the 21st century. However, since many of these potentially therapeutic candidates are promising in the test tube or biochemically, they are not absorbed by living cells or may actually be toxic on delivery. This possible inefficacy has lead pharmaceutical manufacturers to perform tests for Absorption, Distribution, Metabolism, Excretion, and Toxicity or ADMET as early in the discovery process as possible in order to minimize costs by rejecting many treatment candidates. This can also speed up the entire discovery process since companies don’t waste valuable time resources in the evaluation process.

In the early 1990s, the development of CCD cameras (charge coupled device cameras) for research created the opportunity to measure features in images of cells – such as how much protein is in the nucleus, how much is outside. Sophisticated measurements soon followed using new fluorescent molecules, which are used to measure cell properties like second messenger concentrations or the pH of internal cell compartments. This process is known as High Content Screening (High Content Analysis) because much more data about ADMET can be gathered simultaneously from living cells in culture and not just on molecular interactions testing in solution.

Many systems for performing High Content Analysis are available on the market today and many of them use CCD imagers to capture images of cells in vitro either stained or unstained, immunolabeled or not. Immmunolabeling is often done with weak fluorescence emitters or with multicolor labels.

Because the imaging signal may be weak, camera sensitivity and low noise are critical performance factors in the choice of the camera included in the system. Evaluating many thousands of microplate wells in which the cells are grown makes speed of acquisition and processing critical. And, because the cell growth plates are several inches on a side, maintenance of precise focus is vital for successful testing.

Large fields of view can also contribute to the speed of image acquisition.

In this market, DVC offers a variety of cameras which give the system manufacturer a choice of sensor resolution and readout speed while maintaining low noise/high sensitivity levels in the image.

The DVC-2000 is a 1600 by 1200 (2 megapixel) high-QE, low noise camera with 20 and 40 MHz readout speed yielding a full-frame acquisition rate of up to 18 frames per second.

DVC 2000 camera High Content Screening and Analysis
DVC-2000

 

The DVC-4000 Dual Port camera is a 2048 x 2048 (4 megapixel) high-QE, low noise camera. It offers software selectable 20/40 MHz clocking and single/dual-port readout capability. This camera is capable of providing a full-frame acquisition rate of up to 14.5 frames per second.

DVC 4000 dual port camera High Content Screening and Analysis
DVC-4000 Dual Port

References

 

High-Content Screening: A New Approach to Easing Key Bottlenecks in the Drug Discovery Process

Kenneth A. Giuliano
BioDx, Inc., 635 William Pitt Way, Pittsburgh, PA 15238

Robbin L. DeBiasio
BioDx, Inc., 635 William Pitt Way, Pittsburgh, PA 15238

R. Terry Dunlay
BioDx, Inc., 635 William Pitt Way, Pittsburgh, PA 15238

Albert Gough
BioDx, Inc., 635 William Pitt Way, Pittsburgh, PA 15238

Joanne M. Volosky
BioDx, Inc., 635 William Pitt Way, Pittsburgh, PA 15238

Joseph Zock
BioDx, Inc., 635 William Pitt Way, Pittsburgh, PA 15238

George N. Pavlakis
ABL-Basic Research Program, National Cancer Institute-FCRDC, Frederick, MD 21702

D. Lansing Taylor
BioDx, Inc., 635 William Pitt Way, Pittsburgh, PA 15238

Automated Image Analysis for High-Content Screening and Analysis

  1. Aabid Shariff1,2
  2. Joshua Kangas1,2
  3. Luis Pedro Coelho1,2
  4. Shannon Quinn1,3
  5. Robert F. Murphy1,2,3,4,5
1Lane Center for Computational Biology and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA.
2Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program in Computational Biology, Pittsburgh, PA.
3Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA.
4Departments of Biomedical Engineering and Machine Learning, Carnegie Mellon University, Pittsburgh, PA.
5Freiburg Institute for Advanced Studies, Albert Ludwig University of Freiburg, Freiburg, Germany.

Lane Center for Computational Biology Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213 E-mail: murphy@cmu.edu

Automated microscope system for determining factors that predict neuronal fate

Montserrat Arrasate

Steven Finkbeiner
*Gladstone Institute of Neurological Disease, 1650 Owens Street, San Francisco, CA 94158; and Departments of Neurology and Physiology, Neuroscience, and §Biomedical Science Programs, University of California, San Francisco, CA 94114