Perivascular neurons instruct 3D vascular lattice formation via neurovascular contact.

The vasculature of the central nervous system is a 3D lattice composed of laminar vascular beds interconnected by penetrating vessels. The mechanisms controlling 3D lattice network formation remain largely unknown. Combining viral labeling, genetic marking, and single-cell profiling in the mouse retina, we discovered a perivascular neuronal subset, annotated as Fam19a4/Nts-positive retinal ganglion cells (Fam19a4/Nts-RGCs), directly contacting the vasculature with perisomatic endfeet. Developmental ablation of Fam19a4/Nts-RGCs led to disoriented growth of penetrating vessels near the ganglion cell layer (GCL), leading to a disorganized 3D vascular lattice. We identified enriched PIEZO2 expression in Fam19a4/Nts-RGCs. Piezo2 loss from all retinal neurons or Fam19a4/Nts-RGCs abolished the direct neurovascular contacts and phenocopied the Fam19a4/Nts-RGC ablation deficits. The defective vascular structure led to reduced capillary perfusion and sensitized the retina to ischemic insults. Furthermore, we uncovered a Piezo2-dependent perivascular granule cell subset for cerebellar vascular patterning, indicating neuronal Piezo2-dependent 3D vascular patterning in the brain.

Sub-Cellular Dynamic Analysis of BGC823 Cells after Treatment with the Multi-Component Drug CKI Using Raman Spectroscopy.

Multi-component drugs (MCDs) can induce various cellular changes covering multiple levels, from molecular and subcellular structure to cell morphology. A "non-invasive" method for comprehensively detecting the dynamic changes of cellular fine structure and chemical components on the subcellular level is highly desirable for MCD studies. In this study, the subcellular dynamic processes of gastric cancer BGC823 cells after treatment with a multi-component drug, Compound Kushen Injection (CKI), were investigated using a homemade, high-resolution, confocal Raman spectroscopy (RS) device combined with bright-field imaging. The Raman spectra of the nucleus, cytoplasm and intracellular vesicles (0.4-1 µm) were collected simultaneously for each cell treated with CKI at different times and doses. The RS measurements showed that CKI decreased the DNA signatures, which the drug is known to inhibit. Meanwhile, the CKI-induced subcellular dynamic changes in the appearance of numerous intracellular vesicles and the deconstruction of cytoplasm components were observed and discussed. The results demonstrated that high-resolution subcellular micro-Raman spectroscopy has potential for detecting fine cellular dynamic variation induced by drugs and the screening of MCDs in cancer therapy.

Fast label-free recognition of NRBCs by deep-learning visual object detection and single-cell Raman spectroscopy.

Nucleated red blood cells (NRBCs) as a type of rare cell present in an adult's peripheral blood is a concern in hematology, intensive care medicine and prenatal diagnostics. However, it is labor-intensive to screen such rare cells from real complex cell mixtures especially in a label-free way. Herein, we report a new label-free method that incorporates image recognition and Raman spectroscopy for fast recognition of the rare cells in blood. First, we identified unlabeled NRBCs based on both Raman signals of hemoglobin and nucleated morphology, and recorded their microscopic image characteristics which were different enough from other blood cells in unlabeled morphology. Then, two deep-learning algorithms of visual object detection, Faster RCNN and YOLOv3, were investigated for cell morphological recognition on a low-cost computer configuration, and YOLOv3 was demonstrated to be more competent for real-time detection despite slightly lower precision. Finally, several NRBCs were successfully found in maternal blood using this method, which verified the methodological feasibility. Thus, we believe such a labor-saving approach might inspire a new idea for detecting rare cells from complex cell mixtures in a label-free and computer-assisted way.

Correction: Fast label-free recognition of NRBCs by deep-learning visual object detection and single-cell Raman spectroscopy.

Correction for 'Fast label-free recognition of NRBCs by deep-learning visual object detection and single-cell Raman spectroscopy' by Teng Fang et al., Analyst, 2022, https://doi.org/10.1039/D2AN00024E.

Single-Cell Multimodal Analytical Approach by Integrating Raman Optical Tweezers and RNA Sequencing.

Single-cell analysis has become a state-of-art approach to heterogeneity profiling in tumor cells. Herein, we realize a kind of single-cell multimodal analytical approach by combining single-cell RNA sequencing (scRNA-seq) with Raman optical tweezers (ROT), a label-free single-cell identification and isolation technique, and apply it to investigate drug sensitivity. The drug sensitivity of human BGC823 gastric cancer cells toward different drugs, paclitaxel and sodium dichloroacetate, was distinguished in the conjoint analytical way including morphology monitoring, Raman identification, and transcriptomic profiling. Each individual BGC823 cancer cell was measured by Raman spectroscopy, then nondestructively isolated out by ROT, and finally RNA-sequenced. Our results demonstrate each analytical mode can reflect cell response to the drugs from different perspectives and is consistent and complementary with each other. Therefore, we believe the multimodal analytical approach offers an access to comprehensive characterizations of the unicellular complexity, which especially makes sense for studying tumor heterogeneity or a desired special cell from a mixture cell sample such as whole blood.

Nondestructive Identification and Accurate Isolation of Single Cells through a Chip with Raman Optical Tweezers.

Raman optical tweezers (ROT) as a label-free technique plays an important role in single-cell study such as heterogeneity of tumor and microbial cells. Herein we designed a chip utilizing ROT to isolate a specific single cell. The chip was made from a polydimethylsiloxane (PDMS) slab and formed into a gourd-shaped reservoir with a connected channel on a cover glass. On the chip an individual cell could be isolated from a cell crowd and then extracted with ∼0.5 µL of phosphate-buffered saline (PBS) via pipet immediately after Raman spectral measurements of the same cell. As verification, we separated four different type of cells including BGC823 gastric cancer cells, erythrocytes, lymphocytes, and E. coli cells and quantifiably characterized the heterogeneity of the cancer cells, leukocyte subtype, and erythrocyte status, respectively. The average time of identifying and isolating a specific cell was 3 min. Cell morphology comparison and viability tests showed that the successful rate of single-cell isolation was about 90%. Thus, we believe our platform could further couple other single-cell techniques such as single-cell sequencing and become a multiperspective analytical approach at the level of a single cell.

Anti-Cancer Drug Sensitivity Assay with Quantitative Heterogeneity Testing Using Single-Cell Raman Spectroscopy.

A novel anti-cancer drug sensitivity testing (DST) approach was developed based on in vitro single-cell Raman spectrum intensity (RSI). Generally, the intensity of Raman spectra (RS) for a single living cell treated with drugs positively relates to the sensitivity of the cells to the drugs. In this study, five cancer cell lines (BGC 823, SGC 7901, MGC 803, AGS, and NCI-N87) were exposed to three cytotoxic compounds or to combinations of these compounds, and then they were evaluated for their responses with RSI. The results of RSI were consistent with conventional DST methods. The parametric correlation coefficient for the RSI and Methylthiazolyl tetrazolium assay (MTT) was 0.8558 ± 0.0850, and the coefficient of determination was calculated as R² = 0.9529 ± 0.0355 for fitting the dose⁻response curve. Moreover, RSI data for NCI-N87 cells treated by trastuzumab, everolimus (cytostatic), and these drugs in combination demonstrated that the RSI method was suitable for testing the sensitivity of cytostatic drugs. Furthermore, a heterogeneity coefficient H was introduced for quantitative characterization of the heterogeneity of cancer cells treated by drugs. The largest possible variance between RSs of cancer cells were quantitatively obtained using eigenvalues of principal component analysis (PCA). The ratio of H between resistant cells and sensitive cells was greater than 1.5, which suggested the H-value was effective to describe the heterogeneity of cancer cells. Briefly, the RSI method might be a powerful tool for simple and rapid detection of the sensitivity of tumor cells to anti-cancer drugs and the heterogeneity of their responses to these drugs.