Organelles are highly dynamic and fulfill their function by constant motion and cooperation with each other. Current methods rely on fluorescence, leading to short observation time (via photobleaching) and experimental complexity (via multiple-labeling). While label-free microscopes promise a paradigm change in this regard, the spatiotemporal resolutions and specificity are still insufficient to study organelle interactions. Using mitochondria and lysosome as examples, the paper demonstrates that organelle-specific phase contrast microscopy (OS-PCM) can achieve automatic analysis of dynamic metrics of multiple organelles as well as their interactions from unlabeled cells for the first time. Compared to fluorescence-based methods, this method is gentle and holds great promise for label-free visualization and analysis of pan-organelle dynamics and interactions, with minimum perturbation to the cell.
Anemia affects more than ¼ of the world’s population, mostly concentrated in low-resource areas, and carries serious health risks. Yet current screening methods are inadequate due to their inability to separate iron deficiency anemia (IDA) from genetic anemias such as thalassemia trait (TT), thus preventing targeted supplementation of oral iron. Here we present a cost-effective and accurate approach to diagnose anemia and anemia type using measures of cell morphology determined through machine learning applied to optical light scattering measurements. A partial least squares model shows that our system can accurately extract mean cell volume, red cell size heterogeneity, and mean cell hemoglobin concentration with high accuracy. These clinical parameters (or the raw data itself) can be submitted to machine learning algorithms such as quadratic discriminants or support vector machines to classify a patient into healthy, IDA, or TT. A clinical trial conducted on over 268 Chinese children, of which 49 had IDA and 24 had TT, shows >98% sensitivity and specificity for diagnosing anemia, with 81% sensitivity and 86% specificity for discriminating IDA and TT. The majority of the misdiagnoses are IDA patients with particularly severe anemia, possibly requring hospital care. Therefore, in a screening paradigm where anyone testing positive for TT is sent to the hospital for gold-standard diagnosis and care, we maximize patient benefit while minimizing use of scarce resources.
We report the development of a cost-effective, automated parasite diagnostic system that does not require special sample preparation or a trained user. It is composed of a cost-effective, portable microscope that can automatically auto-focus and scan over the size of an entire McMaster chamber (100 mm2) and capture high resolution (~1 µm) bright field images without need for user intervention. Fecal samples prepared using the McMaster flotation method were imaged, with the imaging region comprising the entire McMaster chamber. A convolutional neural network (CNN) automatically segments and analyzes the images to robustly separate eggs from background debris. The performance of the CNN is high despite the challenging, unbalanced nature of the images, where >95% of images contain no eggs and thus the potential for false-positives is high. Simple post-processing of the CNN output yields both egg species and egg counts. The system was validated by comparing hand counts with automated counts of samples containing eggs from ascarid, strongyle, and Trichuris nematodes, along with Eimeria oocysts. The system shows excellent performance, even on challenging Eimeria parasites whose small size is similar to fecal debris. The R2 values between hand and automated counts are >0.95 for both Eimeria and nematode parasites. Further, the diagnostic accuracy of our system for recommending antibiotic treatment is 100% for nematode parasites and 96% for Eimeria. As a further demonstration of utility, the system was used to conveniently quantify drug response over time, showing residual disease due to antibiotic resistance after 2 weeks.
Current flow-based blood counting devices require expensive and centralized medical infrastructure and are not appropriate for field use. In this paper we report a method to count red blood cells, white blood cells as well as platelets through a low-cost and fully-automated blood counting system. The approach consists of using a compact, custom-built microscope with large field-of-view to record bright-field and fluorescence images of samples that are diluted with a single, stable reagent mixture and counted using automatic algorithms. Sample collection is performed manually using a spring loaded lancet, and volume-metering capillary tubes. The capillaries are then dropped into a tube of pre-measured reagents and gently shaken for 10-30 seconds. The sample is loaded into a measurement chamber and placed on a custom 3D printed platform. Sample translation and focusing is fully automated, and a user has only to press a button for the measurement and analysis to commence. Cost of the system is minimized through the use of custom-designed motorized components. We performed a series of comparative experiments by trained and untrained users on blood from adults and children. We compare the performance of our system, as operated by trained and untrained users, to the clinical gold standard using a Bland-Altman analysis, demonstrating good agreement of our system to the clinical standard. The system’s low cost, complete automation, and good field performance indicate that it can be successfully translated for use in low-resource settings where central hematology laboratories are not accessible.
Currently, one-third of humanity is still suffering from anemia. In China the most common forms of anemia are iron deficiency and Thalassemia minor. Differentiating these two is the key to effective treatment. Iron deficiency is caused by malnutrition and can be cured by iron supplementation. Thalassemia is a hereditary disease in which the hemoglobin β chain is lowered or absent. Iron therapy is not effective, and there is evidence that iron therapy may be harmful to patients with Thalassemia. Both anemias can be diagnosed using red blood cell morphology: Iron deficiency presents a smaller mean cell volume compared to normal cells, but with a wide distribution; Thalassemia, meanwhile, presents a very small cell size and tight particle size distribution. Several researchers have proposed diagnostic indices based on red cell morphology to differentiate these two diseases. However, these indices lack sensitivity and specificity and are constructed without statistical rigor. Using multivariate methods we demonstrate a new classification method based on red cell morphology that diagnoses anemia in a Chinese population with enough accuracy for its use as a screening method. We further demonstrate a low cost instrument that precisely measures red cell morphology using elastic light scattering. This instrument is combined with an automated analysis program that processes scattering data to report red cell morphology without the need for user intervention. Despite using consumer-grade components, when comparing our experimental results with gold-standard measurements, the device can still achieve the high precision required for sensing clinically significant changes in red cell morphology.
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