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.
Raman spectroscopy reflects molecular vibrational fingerprints of highly narrow bands. Thus Raman microscopy is well-known for offering an attractive solution to multiplex cellular imaging. However, despite the multiplex capability, Raman imaging has never gotten close to the dream of observing high-speed cellular dynamics that are truly significant for biological studies, due to the fundamental bottleneck of low strength of Raman scattering signals. Even with highly advanced Raman techniques, such as stimulated Raman scattering (SRS) microscopy, researchers have rarely achieved dynamic imaging or at the cost of high laser powers and complex systems, along with sacrificing the full-spectrum information that is the major advantage of Raman spectroscopy. This manuscript summarized our efforts on the improvement of high quality spontaneous Raman imaging, which were recently published on the high-impact journals. We combined novel azo-enhanced Raman scattering (AERS) probes with a highly efficient and speed-optimized line-scan Raman imaging system. The AERS probes targeted cellular organelles and featured back-ground free Raman signals with strength 4 orders of magnitude larger than the traditional Raman probes, e.g. 5-ethynyl-2’-deoxyuridine (EdU), without resorting to the complex coherent Raman approaches. The dynamic azo-enhanced Raman imaging (DAERI) system was able to obtain Raman images of live cells at the temporal resolution of 3.5 s per frame and the confocal spatial resolution of ~270 nm using a low power density of 75 μW/μm2. Empowered by this performance, we demonstrated DAERI of lysosomal and mitochondrial dynamics within live cells. In particular, DAERI’ s preservation of the full-spectrum information utilized the hallmark of spontaneous Raman signals in fingerprint region and allowed us to easily and accurately unmix triple-stained cell images by resolving individual organelles clearly in a single image scan.
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.
We propose a portable fluorescence microscopic imaging system (PFMS) for intraoperative display of biliary structure and prevention of iatrogenic injuries during cholecystectomy. The system consists of a light source module, a camera module, and a Raspberry Pi computer with an LCD. Indocyanine green (ICG) is used as a fluorescent contrast agent for experimental validation of the system. Fluorescence intensities of the ICG aqueous solution at different concentration levels are acquired by our PFMS and compared with those of a commercial Xenogen IVIS system. We study the fluorescence detection depth by superposing different thicknesses of chicken breast on an ICG-loaded agar phantom. We verify the technical feasibility for identifying potential iatrogenic injury in cholecystectomy using a rat model in vivo. The proposed PFMS system is portable, inexpensive, and suitable for deployment in resource-limited settings.
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.
Tatu Rojalin, Heikki Saari, Petter Somersalo, Saara Laitinen, Mikko Turunen, Tapani Viitala, Sebastian Wachsmann-Hogiu, Zachary Smith, Marjo Yliperttula
The aim of this work is to develop a platform for characterizing extracellular vesicles (EV) by using gold-polymer nanopillar SERS arrays simultaneously circumventing the photoluminescence-related disadvantages of Raman with a time-resolved approach. EVs are rich of biochemical information reporting of, for example, diseased state of the biological system. Currently, straightforward, label-free and fast EV characterization methods with low sample consumption are warranted. In this study, SERS spectra of red blood cell and platelet derived EVs were successfully measured and their biochemical contents analyzed using multivariate data analysis techniques. The developed platform could be conveniently used for EV analytics in general.
There is a pressing need for a phantom standard to calibrate medical optical devices. However, 3D printing of tissue-simulating phantom standard is challenged by lacking of appropriate methods to characterize and reproduce surface topography and optical properties accurately. We have developed a structured light imaging system to characterize surface topography and optical properties (absorption coefficient and reduced scattering coefficient) of 3D tissue-simulating phantoms. The system consisted of a hyperspectral light source, a digital light projector (DLP), a CMOS camera, two polarizers, a rotational stage, a translation stage, a motion controller, and a personal computer. Tissue-simulating phantoms with different structural and optical properties were characterized by the proposed imaging system and validated by a standard integrating sphere system. The experimental results showed that the proposed system was able to achieve pixel-level optical properties with a percentage error of less than 11% for absorption coefficient and less than 7% for reduced scattering coefficient for phantoms without surface curvature. In the meanwhile, 3D topographic profile of the phantom can be effectively reconstructed with an accuracy of less than 1% deviation error. Our study demonstrated that the proposed structured light imaging system has the potential to characterize structural profile and optical properties of 3D tissue-simulating phantoms.
KEYWORDS: Raman spectroscopy, Proteins, Cancer, Chemical analysis, Biomedical optics, Laser spectroscopy, Principal component analysis, Industrial chemicals, Spectroscopy, Medical research
Exosomes are small (~100nm) membrane bound vesicles excreted by cells as part of their normal biological processes. These extracellular vesicles are currently an area of intense research, since they were recently found to carry functional mRNA that allows transfer of proteins and other cellular instructions between cells. Exosomes have been implicated in a wide range of diseases, including cancer. Cancer cells are known to have increased exosome production, and may use those exosomes to prepare remote environments for metastasis. Therefore, there is a strong need to develop characterization methods to help understand the structure and function of these vesicles. However, current techniques, such as proteomics and genomics technologies, rely on aggregating a large amount of exosome material and reporting on chemical content that is averaged over many millions of exosomes. Here we report on the use of laser-tweezers Raman spectroscopy (LTRS) to probe individual vesicles, discovering distinct heterogeneity among exosomes both within a cell line, as well as between different cell lines. Through principal components analysis followed by hierarchical clustering, we have identified four “subpopulations” of exosomes shared across seven cell lines. The key chemical differences between these subpopulations, as determined by spectral analysis of the principal component loadings, are primarily related to membrane composition. Specifically, the differences can be ascribed to cholesterol content, cholesterol to phospholipid ratio, and surface protein expression. Thus, we have shown LTRS to be a powerful method to probe the chemical content of single extracellular vesicles.
Cell counting in human body fluids such as blood, urine, and CSF is a critical step in the diagnostic process for many diseases. Current automated methods for cell counting are based on flow cytometry systems. However, these automated methods are bulky, costly, require significant user expertise, and are not well suited to counting cells in fluids other than blood. Therefore, their use is limited to large central laboratories that process enough volume of blood to recoup the significant capital investment these instruments require. We present in this talk a combination of a (1) low-cost microscope system, (2) simple sample preparation method, and (3) fully automated analysis designed for providing cell counts in blood and body fluids. We show results on both humans and companion and farm animals, showing that accurate red cell, white cell, and platelet counts, as well as hemoglobin concentration, can be accurately obtained in blood, as well as a 3-part white cell differential in human samples. We can also accurately count red and white cells in body fluids with a limit of detection ~3 orders of magnitude smaller than current automated instruments. This method uses less than 1 microliter of blood, and less than 5 microliters of body fluids to make its measurements, making it highly compatible with finger-stick style collections, as well as appropriate for small animals such as laboratory mice where larger volume blood collections are dangerous to the animal’s health.
KEYWORDS: Image segmentation, In vivo imaging, Multispectral imaging, Cervical cancer, Reflectivity, RGB color model, Monte Carlo methods, Tissues, Multimodal imaging, Image enhancement, Image processing, Algorithm development
Cervical cancer is the leading cause of cancer death for women in developing countries. Colposcopy plays an important role in early screening and detection of cervical intraepithelial neoplasia (CIN). In this paper, we developed a multimodal colposcopy system that combines multispectral reflectance, autofluorescence, and RGB imaging for in vivo detection of CIN, which is capable of dynamically recording multimodal data of the same region of interest (ROI). We studied the optical properties of cervical tissue to determine multi-wavelengths for different imaging modalities. Advanced algorithms based on the second derivative spectrum and the fluorescence intensity were developed to differentiate cervical tissue into two categories: squamous normal (SN) and high grade (HG) dysplasia. In the results, the kinetics of cervical reflectance and autofluorescence characteristics pre and post acetic acid application were observed and analyzed, and the image segmentation revealed good consistency with the gold standard of histopathology. Our pilot study demonstrated the clinical potential of this multimodal colposcopic system for in vivo detection of cervical cancer.
Integrated Raman and angular-scattering microscopy (IRAM) is a multimodal platform capable of noninvasively probing both the chemistry and morphology of a single cell without prior labeling. Using this system, we are able to detect activation-dependent changes in the Raman and elastic-scattering signals from CD8+ T cells stimulated with either Staphylococcal enterotoxin B (SEB) or phorbol myristate acetate (PMA). In both cases, results obtained from the IRAM instrument correlate well with results obtained from traditional fluorescence-based flow cytometry for paired samples. SEB-mediated activation was distinguished from resting state in CD8+ T cells by an increase in the number and mean size of small (~500-nm) elastic scatterers as well as a decrease in Raman bands, indicating changes in nuclear content. PMA-mediated activation induced a different profile in CD8+ T cells from SEB, showing a similar increase in small elastic scatterers but a different Raman change, with elevation of cellular protein and lipid bands. These results suggest the potential of this multimodal, label-free optical technique for studying processes in single cells.
A microscope system has been constructed that allows simultaneous acquisition of Raman scattering spectra
and elastic scattering Fourier-plane data. The Raman scattering channel reports on chemical composition of
the microscopic sample while the elastic scattering channel reports on morphological information about the
sample. The system has been validated by acquiring data from single polystyrene beads and analyzing the
elastic scattering signal using Generalized Lorenz-Mie Theory while comparing the Raman scattering signature
to other polystyrene spectra from the literature. Monocytes and neutrophils, two immune cell types, have also
been studied and show clear chemical and morphological differences between cell types.
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