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Identification of lymphocyte cell types are crucial for understanding their pathophysiological roles in human diseases. Current methods for discriminating lymphocyte cell types primarily rely on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs for sample preparation and may also have a potential risk of altering cellular functions. Here, we present the identification of non-activated lymphocyte cell types at the single-cell level using refractive index (RI) tomography and machine learning. From the measurements of three-dimensional RI maps of individual lymphocytes, the morphological and biochemical properties of the cells are quantitatively retrieved. To construct cell type classification models, various statistical classification algorithms are compared, and the k-NN (k = 4) algorithm was selected. The algorithm combines multiple quantitative characteristics of the lymphocyte to construct the cell type classifiers. After optimizing the feature sets via cross-validation, the trained classifiers enable identification of three lymphocyte cell types (B, CD4+ T, and CD8+ T cells) with high sensitivity and specificity. The present method, which combines RI tomography and machine learning for the first time to our knowledge, could be a versatile tool for investigating the pathophysiological roles of lymphocytes in various diseases including cancers, autoimmune diseases, and virus infections.

A synergistic application of holography and deep learning enables rapid optical screening of anthrax spores and other pathogens.

Humidity sensors are essential components in wearable electronics for monitoring of environmental condition and physical state. In this work, a unique humidity sensing layer composed of nitrogen-doped reduced graphene oxide (nRGO) fiber on colorless polyimide film is proposed. Ultralong graphene oxide (GO) fibers are synthesized by solution assembly of large GO sheets assisted by lyotropic liquid crystal behavior. Chemical modification by nitrogen-doping is carried out under thermal annealing in H (4%)/N (96%) ambient to obtain highly conductive nRGO fiber. Very small (≈2 nm) Pt nanoparticles are tightly anchored on the surface of the nRGO fiber as water dissociation catalysts by an optical sintering process. As a result, nRGO fiber can effectively detect wide humidity levels in the range of 6.1-66.4% relative humidity (RH). Furthermore, a 1.36-fold higher sensitivity (4.51%) at 66.4% RH is achieved using a Pt functionalized nRGO fiber (i.e., Pt-nRGO fiber) compared with the sensitivity (3.53% at 66.4% RH) of pure nRGO fiber. Real-time and portable humidity sensing characteristics are successfully demonstrated toward exhaled breath using Pt-nRGO fiber integrated on a portable sensing module. The Pt-nRGO fiber with high sensitivity and wide range of humidity detection levels offers a new sensing platform for wearable humidity sensors.

Homeostasis of neutrophils—the blood cells that respond first to infection and tissue injury—is critical for the regulation of immune responses and regulated through granulopoiesis, a multi-stage process by which neutrophils differentiate from hematopoietic stem cells. Granulopoiesis is a highly dynamic process and altered in certain clinical conditions, such as pathologic and iatrogenic neutropenia, described as demand-adapted granulopoiesis. The regulation of granulopoiesis under stress is not completely understood because studies of granulopoiesis dynamics have been hampered by technical limitations in defining neutrophil precursors. Here, we define a population of neutrophil precursor cells in the bone marrow with unprecedented purity, characterized by the lineage−CD11b+Ly6GloLy6BintCD115−, which we call NeuPs (Neutrophil Precursors). We demonstrated that NeuPs differentiate into mature and functional neutrophils both in vitro and in vivo. By analyzing the gene expression profiles of NeuPs, we also identified NeuP stage-specific genes and characterized patterns of gene regulation throughout granulopoiesis. Importantly, we found that NeuPs have the potential to proliferate, but the proliferation decreased in multiple different hematopoietic stress settings, indicating that proliferating NeuPs are poised at a critical step to regulate granulopoiesis. Our findings will facilitate understanding how the hematopoietic system maintains homeostasis and copes with the demands of granulopoiesis.

Rapid identification of bacterial species is crucial in medicine and food hygiene. In order to achieve rapid and label-free identification of bacterial species at the single bacterium level, we propose and experimentally demonstrate an optical method based on Fourier transform light scattering (FTLS) measurements and statistical classification. For individual rod-shaped bacteria belonging to four bacterial species (Listeria monocytogenes, Escherichia coli, Lactobacillus casei, and Bacillus subtilis), two-dimensional angle-resolved light scattering maps are precisely measured using FTLS technique. The scattering maps are then systematically analyzed, employing statistical classification in order to extract the unique fingerprint patterns for each species, so that a new unidentified bacterium can be identified by a single light scattering measurement. The single-bacterial and label-free nature of our method suggests wide applicability for rapid point-of-care bacterial diagnosis.

PtO nanocatalysts-loaded SnO multichannel nanofibers (PtO-SnO MCNFs) were synthesized by single-spinneret electrospinning combined with apoferritin and two immiscible polymers, i.e., poly(vinylpyrrolidone) and polyacrylonitrile. The apoferritin, which can encapsulate nanoparticles within a small inner cavity (8 nm), was used as a catalyst loading template for an effective functionalization of the PtO catalysts. Taking advantage of the multichannel structure with a high porosity, effective activation of catalysts on both interior and exterior site of MCNFs was realized. As a result, under high humidity condition (95% RH), PtO-SnO MCNFs exhibited a remarkably high acetone response (R/R = 194.15) toward 5 ppm acetone gases, superior selectivity to acetone molecules among various interfering gas species, and excellent stability during 30 cycles of response and recovery toward 1 ppm acetone gases. In this work, we first demonstrate the high suitability of multichannel semiconducting metal oxides structure functionalized by apoferritin-encapsulated catalytic nanoparticles as highly sensitive and selective gas-sensing layer.

Establishing early warning systems for anthrax attacks is crucial in biodefense. Here we present an optical method for rapid screening of Bacillus anthracis spores through the synergistic application of holographic microscopy and deep learning. A deep convolutional neural network is designed to classify holographic images of unlabeled living cells. After training, the network outperforms previous techniques in all accuracy measures, achieving single-spore sensitivity and sub-genus specificity. The unique 'representation learning' capability of deep learning enables direct training from raw images instead of manually extracted features. The method automatically recognizes key biological traits encoded in the images and exploits them as fingerprints. This remarkable learning ability makes the proposed method readily applicable to classifying various single cells in addition to B. anthracis, as demonstrated for the diagnosis of Listeria monocytogenes, without any modification. We believe that our strategy will make holographic microscopy more accessible to medical doctors and biomedical scientists for easy, rapid, and accurate diagnosis of pathogens, and facilitate exciting new applications.