Back

NOMA: a high-throughput microchip for robust, sequential measurements of secretions from the same single-cells

Liu, M.; Ji, Y.; Zhu, F.; Bai, X.; Li, L.; Xie, H.; Wei, Y.; Liu, X.; Luo, Y.; Liu, T.; Lin, B.; Lu, Y.

2021-07-14 bioengineering
10.1101/2021.01.21.427049 bioRxiv
Show abstract

Despite recent advances in single-cell analysis technologies, lacking simple methods to keep the live single-cells traceable for longitudinal detection reliably poses a significant obstacle in single-cell secretion analysis. Here we reported the high-density NOMA (narrow-opening microwell array) microchip that realized the retention of[≥] 97% of trapped single cells in dedicated spatial locations during repetitive detection procedures, verified with both adherent and suspension cells by two researchers independently. We applied it in monitoring single-cell protein secretions sequentially from the same single cells, and we found the digital protein secretion patterns dominate the protein secretion. We also demonstrated the microchip for longitudinally tracking IL-8 and the CD81+EV secretions from the same single-cells over days, which revealed the presence of "super secretors" within the cell population be more persistent to secrete protein or extracellular vesicle for an extended period. The NOMA platform reported here is simple, robust, and easy to operate for tracking sequential measurements from the same single cells, representing a novel and informative tool to inspire new observations in biomedical research. Table of Contents Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=66 SRC="FIGDIR/small/427049v4_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@aa69c7org.highwire.dtl.DTLVardef@6bc79org.highwire.dtl.DTLVardef@3fcbbforg.highwire.dtl.DTLVardef@597f4b_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.