Counting Infected Blood Cells with Your Cell Phone

Using standard CCD photo camera light sensors, without utilizing any lens optics, scientists at the University of California, Los Angeles are able to distinguish between normal and infected cells in blood samples. The technique, developed, and now improved, by Dr.Aydogan Ozcan and colleagues from the California NanoSystems Institute at UCLA, is called LUCAS, or Lensless Ultra-wide-field Cell monitoring Array. It is in essence a diffraction shadow imaging modality.
Here’s what UCLA says about the research:

First published in the Royal Society of Chemistry’s journal Lab Chip in 2007, the LUCAS technique, developed by UCLA researchers, demonstrated a lens-free method for quickly and accurately counting targeted cell types in a homogenous cell solution. Removing the lens from the imaging process allows LUCAS to be scaled down to the point that it can eventually be integrated into a regular wireless cell phone. Samples could be loaded into a specially equipped phone using a disposable microfluidic chip.
The UCLA researchers have now improved the LUCAS technique to the point that it can classify a significantly larger sample volume than previously shown — up to 5 milliliters, from an earlier volume of less than 0.1 ml — representing a major step toward portable medical diagnostic applications…
Ozcan envisions people one day being able to draw a blood sample into a chip the size of a quarter, which could then be inserted into a LUCAS-equipped cell phone that would quickly identify and count the cells within the sample. The read-out could be sent wirelessly to a hospital for further analysis.
"This on-chip imaging platform may have a significant impact, especially for medical diagnostic applications related to global health problems such as HIV or malaria monitoring," Ozcan said.
LUCAS functions as an imaging scheme in which the shadow of each cell in an entire sample volume is detected in less than a second. The acquired shadow image is then digitally processed using a custom-developed "decision algorithm" to enable both the identification of the cell/bacteria location in 3-D and the classification of each microparticle type within the sample volume.
Various cell types — such as red blood cells, fibroblasts and hepatocytes — or other microparticles, such as bacteria, all exhibit uniquely different shadow patterns and therefore can be rapidly identified using the decision algorithm.
The new study demonstrates that the use of narrowband, short-wavelength illumination significantly improves the detection of cell shadow images. Furthermore, by varying the wavelength, the two-dimensional pattern of the recorded cell signatures can be tuned to enable automated identification and counting of a target cell type within a mixed cell solution.
"This is the first demonstration of automated, lens-free counting and characterization of a mixed, or heterogeneous, cell solution on a chip and holds significant promise for telemedicine applications," Ozcan said.
Another improvement detailed in the UCLA research is the creation of a hybrid imaging scheme that combines two different wavelengths to further improve the digital quality of shadow images. This new cell classification scheme has been termed "multicolor LUCAS." As the team illustrated, further improvement in image quality can also be achieved through the use of adaptive digital filtering. As result of these upgrades, the volume of the sample solution that can be imaged has been increased, as mentioned, from less than 0.1 ml to 5 ml.

Press release: Better health through your cell phone…
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Abstract: Multi-color LUCAS : Lensfree On-chip Cytometry Using Tunable Monochromatic Illumination and Digital Noise Reduction
Image: A new cell counter uses the imaging chip from a digital camera to record the “shadows,” or diffraction signatures, from cells in blood and other samples. Simple algorithms allow cells to be identified and counted because each cell type has a unique signature. In this image taken with the cell counter, yeast cells are circled in blue, red blood cells are circled in red, and beads are circled in green. Credit: Aydogan Ozcan
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