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Single-cell RNA sequencing (scRNA-seq) is one of the most efficient technologies for human tumor research. However, data analysis is still faced with technical challenges, especially the difficulty in efficiently and accurately discriminating cancer/normal cells in the scRNA-seq expression matrix. If we can address these challenges, we can have a deeper understanding of the intratumoral and intertumoral heterogeneity. In this study, we developed a cancer/normal cell discrimination pipeline called pan-Cancer Seeker (CaSee) devoted to scRNA-seq expression matrix, which is based on the traditional high-quality pan-cancer bulk sequencing data using transfer learning. CaSee is the first tool directly used to discriminate cancer/normal cells in the scRNA-seq expression matrix, with much wider application fields and higher efficiency than copy number variation (CNV) method which requires corresponding reference cells. CaSee is user-friendly and can adapt to a variety of data sources, including but not limited to scRNA tissue sequencing data, scRNA cell line sequencing data, scRNA xenograft cell sequencing data and scRNA circulating tumor cell sequencing data. It is compatible with mainstream sequencing technology platforms, 10× Genomics Chromium, Smart-seq2, and Microwell-seq. Here, CaSee pipeline exhibited excellent performance in the multicenter data evaluation of 11 retrospective cohorts and one independent dataset, with an average discrimination accuracy of 96.69%. In general, the development of a deep-learning based, pan-cancer cell discrimination model, CaSee, to distinguish cancer cells from normal cells will be compelling to researchers working in the genomics, cancer, and single-cell fields.

The monitoring of circulating tumor cells (CTCs) has recently served as a promising approach for assessing prognosis and evaluating cancer treatment. We have already developed a CTCs enrichment platform by EpCAM recognition peptide-functionalized magnetic nanoparticles (EP@MNPs). However, considering heterogeneous CTCs generated through epithelial-mesenchymal transition (EMT), mesenchymal CTCs would be missed with this method. Notably, N-cadherin, overexpressed on mesenchymal CTCs, can facilitate the migration of cancer cells. Hence, we screened a novel peptide targeting N-cadherin, NP, and developed a new CTCs isolation approach via NP@MNPs to complement EpCAM methods' deficiencies. NP@MNPs had a high capture efficiency (about 85%) of mesenchymal CTCs from spiked human blood. Subsequently, CTCs were captured and sequenced at the single-cell level via NP@MNPs and EP@MNPs, RNA profiles of which showed that epithelial and mesenchymal subgroups could be distinguished. Here, a novel CTCs isolation platform laid the foundation for mesenchymal CTCs isolation and subsequent molecular analysis.

A simple, accurate and rapid method for simultaneous analysis of vancomycin and ceftazidime in cerebrospinal fluid (CSF), utilizing high-performance liquid chromatography (HPLC), has been developed and thoroughly validated to satisfy strict FDA guidelines for bioanalytical methods. Protein precipitation was used as the sample pretreatment method. In order to increase the accuracy, tinidazole was chosen as the internal standard. Separation was achieved on a Diamonsil C18 column (200 mm x 4.6mm I.D., 5 microm) using a mobile phase composed of acetonitrile and acetate buffer (pH 3.5) (8:92, v/v) at room temperature (25 degrees C), and the detection wavelength was 240 nm. All the validation data, such as accuracy, precision, and inter-day repeatability, were within the required limits. The method was applied to determine vancomycin and ceftazidime concentrations in CSF in five craniotomy patients.