DNA Nanolithography Enables a Highly Ordered Recognition Interface in a Microfluidic Chip for the Efficient Capture and Release of Circulating Tumor Cells

Single-Cell Liquid Biopsy of Lung Cancer: Ultra-Simplified Efficient Enrichment of Circulating Tumor Cells and Hand-Held Fluorometer Portable Testing

Herein, we propose a paper-based laboratory via enzyme-free nucleic acid amplification and nanomaterial-assisted cation exchange reactions (CERs) assisted single-cell-level analysis (PLACS). This method allowed for the rapid detection of mucin 1 and trace circulating tumor cells (CTCs) in the peripheral blood of lung cancer patients. Initially, an independently developed method requiring one centrifuge, two reagents (lymphocyte separation solution and erythrocyte lysate), and a three-step, 45 min sample pretreatment was employed. The core of the detection approach consisted of two competitive selective identifications: copper sulfide nanoparticles (CuS NPs) to C-Ag+-C and Ag+, and dual quantum dots (QDs) to Cu2+ and CuS NPs. To facilitate multimodal point-of-care testing (POCT), we integrated solution visualization, test strip length reading, and a self-developed hand-held fluorometer readout. These methods were detectable down to ag/mL of mucin 1 concentration and the single-cell level. Forty-seven clinical samples were assayed by fluorometer, yielding 94% (30/32) sensitivity and 100% (15/15) specificity with an area under the curve (AUC) of 0.945. Nine and 15 samples were retested by a test strip and hand-held fluorometer, respectively, with an AUC of 0.95. All test results were consistent with the clinical imaging and the folate receptor (FR)-PCR kit findings, supporting its potential in early diagnosis and postoperative monitoring.

Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing

Spatial transcriptomics technologies with high resolution often lack high sensitivity in mRNA detection. Here we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq), which offers both high sensitivity and high resolution. Decoder-seq combines dendrimeric nanosubstrates with microfluidic coordinate barcoding to generate spatial arrays with a DNA density approximately ten times higher than previously reported methods while maintaining flexibility in resolution. We show that the high RNA capture efficiency of Decoder-seq improved the detection of lowly expressed olfactory receptor (Olfr) genes in mouse olfactory bulbs and contributed to the discovery of a unique layer enrichment pattern for two Olfr genes. The near-cellular resolution provided by Decoder-seq has enabled the construction of a spatial single-cell atlas of the mouse hippocampus, revealing dendrite-enriched mRNAs in neurons. When applying Decoder-seq to human renal cell carcinomas, we dissected the heterogeneous tumor microenvironment across different cancer subtypes and identified spatial gradient-expressed genes related to epithelial-mesenchymal transition with the potential to predict tumor prognosis and progression.

Advances in precise single-cell capture for analysis and biological applications

Cells are the basic structural and functional units of living organisms. However, conventional cell analysis only averages millions of cell populations, and some important information is lost. It is essential to quantitatively characterize the physiology and pathology of single-cell activities. Precise single-cell capture is an extremely challenging task during cell sample preparation. In this review, we summarize the category of technologies to capture single cells precisely with a focus on the latest development in the last five years. Each technology has its own set of benefits and specific challenges, which provide opportunities for researchers in different fields. Accordingly, we introduce the applications of captured single cells in cancer diagnosis, analysis of metabolism and secretion, and disease treatment. Finally, some perspectives are provided on the current development trends, future research directions, and challenges of single-cell capture.