Back

More is better: A simple antibody-based strategy for recovering all major mouse brain cell types from multiplexed single-cell RNAseq samples.

Pratesi, F.; Hamilton, L. K.; Avila Lopez, J.; Aumont, A.; Avino, M.; Singh, N.; Arefiev, I.; Brunet, M. A.; Fernandes, K. J. L.

2024-06-12 molecular biology
10.1101/2024.06.11.598585 bioRxiv
Show abstract

Single-cell RNA sequencing (scRNAseq) is a powerful yet costly technique for studying cellular diversity within the complexity of organs and tissues. Here, we sought to establish an effective multiplexing strategy for the adult mouse brain that could allow multiple experimental groups to be pooled into a single sample for sequencing, reducing costs, increasing data yield, and eliminating batch effects. We first describe an optimized cold temperature single-cell dissociation protocol that permits isolation of a high yield and viability of brain cells from the adult mouse. Cells isolated using this protocol were then screened by flow cytometry using a panel of antibodies, allowing identification of a single antibody, anti-Thy1.2, that can tag the vast majority of isolated mouse brain cells. We then used this primary antibody against a "universal" neural target, together with secondary antibodies carrying sample-specific oligonucleotides and the BD Rhapsody single-cell system and show that multiple adult mouse brain samples can be pooled into a single multiplexed run for scRNAseq. Bioinformatic analyses enable efficient demultiplexing of the sequenced pooled brain sample, with high tagging efficiency and precise annotation and clustering of brain cell populations. The efficiency and flexibility of the cell dissociation protocol and the two-step multiplexing strategy simplifies experimental design, optimizes reagent usage, eliminates sequencing batch effects and reduces overall experimental costs.

Matching journals

The top 5 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.