Two recent papers from the Beckman Institute’s Quantitative Light Imaging laboratory (QLI) are pointing the way toward real-time imaging for medical diagnoses in two important areas. One involves fast imaging of cardiac cell structure and function, while the other reports on the creation of a “digital hematology” system for blood screening that could greatly benefit underserved people in developing areas.
In the latter paper, QLI director Gabriel Popescu, first author Hoa Pham from the lab, and their co-authors report on an automated digital system for testing blood samples remotely and in real time. In the paper, Real time blood testing using quantitative phase imaging, they write that the system enables “remote diagnosis with minimal human intervention in economically challenged areas.”
The method can take advantage of common technology like a cell phone camera – as have others working toward similar solutions – but with a totally new approach: data collection rather than image analysis. This technique not only has the potential to make blood analysis available to millions more people, but it also provides more information on red blood cells than standard measurement instruments like automatic analyzers.
Their system combines the advantages of the lab’s quantitative phase imaging techniques, such as stain-free optical imaging, with parallel computing for processing samples. The technique eliminates the need for traditional lab work like dye staining, as well as microscope analysis of samples by medical personnel. That opens up the prospect of making blood screening available worldwide, including in places with limited access to healthcare.
“It is precisely the absence of technology and clinical expertise that prevents blood testing from becoming universally available,” the authors write.
Devices like cell phone cameras can be used for “telepathology” but the images they produce are large datafiles and require analysis by trained professionals. Instead of clinical evaluation, this new method addresses the problem by utilizing data collection for “computer-controlled, quantitative analysis” that doesn’t require human input on the analysis end. The data numbers rendered by the instrument are for the morphological parameters of the cell, not the image, meaning the method doesn’t require large memory capacity.
A drop of blood can thus be analyzed remotely with a display and user interface on the patient end, then applying quantitative phase imaging and image reconstruction techniques to pictures, including those, for example, taken with a cell phone and sent over the Internet.
Quantitative phase imaging uses diffraction phase microscopy, but employs two beams instead of one for a holographic microscopy technique that gives information on structure and motion, including quantitative data. In this project, the researchers combined the method with the parallel computing software they developed to glean information from pixels of the optical images, and were able to reconstruct images and measure with great sensitivity thousands of cells in a smear film of a blood sample. They got detailed, nanoscale information on single cells in real time on factors such as cell morphology and volume for use as diagnostic parameters.
“Thus, a thousand cells can be analyzed in less than 5 minutes,” they report; in addition, the distilled data outputted by the thousands of images require “only kilobytes of memory per patient and can easily be transmitted wirelessly over the cellular network. This aspect, together with the fact that the blood necessary for this test can be obtained via a simple finger prick (akin to that in a glucose test), we envision that our instrument can operate in areas where clinical expertise and infrastructure are absent.”
The authors added that they “envision that such a system will help democratize access to blood testing and furthermore, may pave the way to digital hematology.”
Co-authors on the paper include Krishnarao Tangella from the University of Illinois Department of Pathology and Christie Clinic and Catherine Best-Popescu from the Illinois College of Medicine.
Both the blood cell paper and the paper on cardiac cell imaging appear in the journal PLoSONE. In the latter paper, titled Cardiomyocyte imaging using real-time spatial light interference microscopy (SLIM), Popescu, first author Basanta Bhaduri from the lab, and their collaborators report on using their Spatial Light Interference Microscopy (SLIM) method for imaging cardiac muscle cells.
SLIM (an interferometric technique employing an add-on module to a commercial phase contrast microscope and developed in the QLI) uses optics to render nanoscale information on specimens. SLIM reveals intrinsic contrast in tissue samples and provides quantitative phase information.
In this project the latter ability provided information on beating cardiomyocyte (heart muscle) cells that enabled the phase reconstruction and display of quantitative phase images in real time. Those images, the researchers wrote, “provide the necessary time resolution for understanding mass transport at the fast scales involved in beating cardiomyocytes.”
The researchers believe that this method can “open new areas of study in label free cell imaging. The real-time SLIM system provides spatially and temporally resolved data describing the mass density distribution of live cells. For the first time, we were able to resolve the fast dynamics of cardiomyocytes…” – a capability which allowed the researchers to “acquire basic knowledge about the physical nature (diffusive vs. deterministic) of cell mass displacements.”
That knowledge revealed that, over the spatial and temporal ranges they studied, transport in these cells was, as expected from the oscillation found in the heart, in fact deterministic (meaning directed, as opposed to diffusive, or random).
“We anticipate that this type of investigation will teach us new aspects of intracellular and intercellular interactions,” they wrote.
Beckman faculty member Rashid Bashir was a co-author of the paper. Popescu and Bashir are faculty in the Department of Electrical and Computer Engineering.