In silico-labeled ghost cytometry.

Characterization and isolation of a large population of cells are indispensable procedures in biological sciences. Flow cytometry is one of the standards that offers a method to characterize and isolate cells at high throughput. When performing flow cytometry, cells are molecularly stained with fluorescent labels to adopt biomolecular specificity which is essential for characterizing cells. However, molecular staining is costly and its chemical toxicity can cause side effects to the cells which becomes a critical issue when the cells are used downstream as medical products or for further analysis. Here, we introduce a high-throughput stain-free flow cytometry called in silico-labeled ghost cytometry which characterizes and sorts cells using machine-predicted labels. Instead of detecting molecular stains, we use machine learning to derive the molecular labels from compressive data obtained with diffractive and scattering imaging methods. By directly using the compressive 'imaging' data, our system can accurately assign the designated label to each cell in real time and perform sorting based on this judgment. With this method, we were able to distinguish different cell states, cell types derived from human induced pluripotent stem (iPS) cells, and subtypes of peripheral white blood cells using only stain-free modalities. Our method will find applications in cell manufacturing for regenerative medicine as well as in cell-based medical diagnostic assays in which fluorescence labeling of the cells is undesirable.

Nonlinear Optics with Near-Infrared Excitation Enable Real-Time Quantitative Diagnosis of Human Cervical Cancers.

Histopathologic analysis through biopsy has been one of the most useful methods for the assessment of malignant neoplasms. However, some aspects of the analysis such as invasiveness, evaluation range, and turnaround time from biopsy to report could be improved. Here, we report a novel method for visualizing human cervical tissue three-dimensionally, without biopsy, fixation, or staining, and with sufficient quality for histologic diagnosis. Near-infrared excitation and nonlinear optics were employed to visualize unstained human epithelial tissues of the cervix uteri by constructing images with third-harmonic generation (THG) and second-harmonic generation (SHG). THG images enabled evaluation of nuclear morphology in a quantitative manner with six parameters after image analysis using deep learning. It was also possible to quantitatively assess intraepithelial fibrotic changes based on SHG images and another deep learning analysis. Using each analytical procedure alone, normal and cancerous tissue were classified quantitatively with an AUC ≥0.92. Moreover, a combinatory analysis of THG and SHG images with a machine learning algorithm allowed accurate classification of three-dimensional image files of normal tissue, intraepithelial neoplasia, and invasive carcinoma with a weighted kappa coefficient of 0.86. Our method enables real-time noninvasive diagnosis of cervical lesions, thus constituting a potential tool to dramatically change early detection. SIGNIFICANCE: This study proposes a novel method for diagnosing cancer using nonlinear optics, which enables visualization of histologic features of living tissues without the need for any biopsy or staining dye.

Generation of primitive neural stem cells from human fibroblasts using a defined set of factors.

In mice, leukemia inhibitory factor (LIF)-dependent primitive neural stem cells (NSCs) have a higher neurogenic potential than bFGF-dependent definitive NSCs. Therefore, expandable primitive NSCs are required for research and for the development of therapeutic strategies for neurological diseases. There is a dearth of suitable techniques for the generation of human long-term expandable primitive NSCs. Here, we have described a method for the conversion of human fibroblasts to LIF-dependent primitive NSCs using a strategy based on techniques for the generation of induced pluripotent stem cells (iPSCs). These LIF-dependent induced NSCs (LD-iNSCs) can be expanded for >100 passages. Long-term cultured LD-iNSCs demonstrated multipotent neural differentiation potential and could generate motor neurons and dopaminergic neurons, as well as astrocytes and oligodendrocytes, indicating a high level of plasticity. Furthermore, LD-iNSCs easily reverted to human iPSCs, indicating that LD-iNSCs are in an intermediate iPSC state. This method may facilitate the generation of patient-specific human neurons for studies and treatment of neurodegenerative diseases.