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Single-cell transcriptomics is essential for understanding biological variability among cells in a heterogenous population. Acquiring high-quality single-cell sequencing data from a tissue sample has multiple challenges including isolation of individual cells as well as amplification of the genetic material. Commercially available techniques require the isolation of individual cells from a tissue through extensive manual manipulation before single cell sequence data can be acquired. However, since cells within a tissue have different dissociation constants, enzymatic and mechanical manipulation do not guarantee the isolation of a homogenous population of cells. To overcome this drawback, in this research we have developed a revolutionary approach that utilizes a fully automated nanopipette technology in combination with magnetic nanoparticles to obtain high quality sequencing reads from individual cells within an intact tissue thereby eliminating the need for manual manipulation and single cell isolation. With the proposed technology, it is possible to sample an individual cell within the tissue multiple times to obtain longitudinal information. Single-cell RNAseq was achieved by aspirating only1-5% of sub-single-cell RNA content from individual cells within fresh frozen tissue samples. As a proof of concept, aspiration was carried out from 22 cells within a breast cancer tissue slice using quartz nanopipettes. The mRNA from the aspirate was then selectively captured using magnetic nanoparticles. The RNAseq data from aspiration of 22 individual cells provided high alignment rates (80%) with 2 control tissue samples. The technology is exceptionally simple, quick and efficient as the entire cell targeting and aspiration process is fully automated.

Abstract Objective Using a system, which accuracy is equivalent to the gold standard Douglas Bag (DB) technique for measuring oxygen consumption (VO2), carbon dioxide generation (VCO2), and respiratory quotient (RQ), we aimed to continuously measure these metabolic indicators and compare the values between post-cardiothoracic surgery and critical care patients. Methods This was a prospective, observational study conducted at a suburban, quaternary care teaching hospital. Age 18 years or older patients who underwent mechanical ventilation were enrolled. Results We included 4 post-surgery and 6 critical care patients. Of those, 3 critical care patients died. The longest measurement reached to 12 h and 15 min and 50 cycles of repeat measurements were performed. VO2 of the post-surgery patients were 234 ± 14, 262 ± 27, 212 ± 16, and 192 ± 20 mL/min, and those of critical care patients were 122 ± 20, 189 ± 9, 191 ± 7, 191 ± 24, 212 ± 12, and 135 ± 21 mL/min, respectively. The value of VO2 was more variable in the post-surgery patients and the range of each patient was 44, 126, 71, and 67, respectively. SOFA scores were higher in non-survivors and there were negative correlations of RQ with SOFA. Conclusions We developed an accurate system that enables continuous and repeat measurements of VO2, VCO2, and RQ. Critical care patients may have less activity in metabolism represented by less variable values of VO2 and VCO2 over time as compared to those of post-cardiothoracic surgery patients. Additionally, an alteration of these values may mean a systemic distinction of the metabolism of critically ill patients.