In recent years, label-free microscopy has gained momentum over the well-established fluorescence microscopy, as it allows overcoming many important drawbacks related to the staining process. Among the label-free imaging techniques, Quantitative Phase Imaging (QPI) has emerged since biophysical properties of cells and tissues are measured. The latest development of QPI is Tomographic Phase Microscopy (TPM), which allows reconstructing the 3D volumetric distribution of the Refractive Indices (RIs) at the single-cell level by combining multiple phase-contrast maps recorded all around the sample. Very recently, the TPM paradigm has been even demonstrated working in Flow Cytometry (FC) modality, thus opening the route to the label-free, 3D, quantitative and high-throughput recording of living suspended cells. Nevertheless, the several advantages of QPI and TPM over fluorescence microscopy are counterbalanced by the lack of intracellular specificity due to the stain-free imaging modality. In fact, the inner cell contrast usually is not enough to properly recognize the several organelles, thus preventing intracellular studies. In QPI and in static TPM, virtual staining has been proposed as a solution, based on the training of deep learning strategies to numerically emulate the chemical staining process. However, the virtual staining approach cannot be replicated in the TPM-FC technique since a dataset of paired 3D RI and fluorescent tomograms of cells cannot be created. Here we show a computational method for the stain-free segmentation of the nucleus in 3D inside the TPM-FC tomograms of flowing cells based on an ad hoc clustering of the intracellular voxels according to their statistical similarities.
Machine learning in combination with microscopy is a well-established paradigm for the identification of cells target (e.g. sick cells) or for the statistical study of cells’ populations. In general, the accuracy in classifying single cells depends on the selected imaging modality, i.e., the more informative it is, the more performant the classifier is. Here we show that the combination of machine learning and holographic microscopy is an effective tool to achieve the above goal, thus allowing higher classification performances if compared to other standard microscopies. Moreover, by exploiting a priori information about the samples to identify, the classification performance can be further increased. We demonstrate this paradigm for the differential diagnosis of hereditary anemias, in which RBCs, imaged by holographic microscopy, are used to predict firstly if an anemia occurs, then which type of anemia among five phenotypes.
The actual gap of the label-free quantitative phase microscopy in respect to fluorescence microscopy, that allows the subcellular characterization by using exogenous markers, is the lack of intracellular specificity. Recently, computational methods based on artificial intelligence have been demonstrated, which allow a virtual staining of single cells in both 2D and 3D, but they require co-registration systems able to collect simultaneously both fluorescence and quantitative phase information. However, a real limitation exists, i.e. these approaches cannot be used in flow cytometry condition. In this paper, we discuss a new methodology for adding the intracellular specificity analysis to tomographic phase microscopy in flow cytometry. The proposed strategy is based on the statistical clustering of tomograms voxels, thus allowing the segmentation of cell’s organelles. Here we report the results of nuclear region identification for cancer cells.
We propose a new diagnostic tool for anemias identification based on quantitative phase imaging. We introduce a panel of label-free optical markers to identify red blood cell (RBC) phenotypes, demonstrating that an optical fingerprint of RBC is related to erythrocyte disease through modeling RBC as biological lens.
Gold standard methods for anaemia diagnosis are the complete blood count and the peripheral smear observation. However, they do not allow for a complete differential diagnosis, which requires biochemical assays, thus being labeldependent techniques. On the other hand, recent studies focus on label-free quantitative phase imaging (QPI) of blood samples to investigate blood diseases by using video-based morphological methods. However, when sick cells are very similar to healthy ones in terms of morphometric features, identification of a blood disease becomes challenging even by morphometric analysis as well as QPI. Here we exploit in-flow tomographic phase microscopy to retrieve the exact 3D rendering of Red Blood Cells (RBCs) from anaemic patients and to identify the pathology, distinguishing it from healthy samples. Moreover, we introduce a Label-free Optical Marker (LOM) to detect RBC phenotypes demonstrating that a single set of all-optical parameters can clearly identify a signature directly related to the erythrocytes disease by modelling each RBC as a biolens. We tested this novel bio-photonic analysis by proofing that several inherited anaemias, specifically Iron-deficiency Anaemia, Thalassemia, Hereditary Spherocytosis and Congenital Dyserythropoietic Anaemia, can be identified and sorted thus opening a novel route for blood diagnosis on a completely different concept based on LOMs.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.