Trying a slightly new model (for us at least) of #openscience. With @ronanchaligne.bsky.social and @karolisk.bsky.social, we pitted 10x Genomics Chromium GEM-X against Illumina PIPseq V head-to-head to understand these two platforms for single-cell genomics. Some thoughts off the bleeding edge: /1 - ThreadSky

caleblareau.bsky.social • 16 days ago

Trying a slightly new model (for us at least) of #openscience. With @ronanchaligne.bsky.social and @karolisk.bsky.social, we pitted 10x Genomics Chromium GEM-X against Illumina PIPseq V head-to-head to understand these two platforms for single-cell genomics. Some thoughts off the bleeding edge: /1

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caleblareau.bsky.social•16 days ago

We've been using 10x products for profiling and technology development for several years now. With new products coming online for single-cell profiling, we wanted to get first-hand experience with PIPseq as it's (imo) the tech that could most easily be integrated into our existing workflows. /2

caleblareau.bsky.social•16 days ago

We designed a basic head-to-head experiment using a cryovial of PBMCs from a healthy donor, split and profiled via 10x GEM-X 3' and PIPseq V (T20). @karolisk.bsky.social masterfully generated data on his first try with the Single-cell Analytics Innovation Lab (SAIL) here at MSKCC. /3

caleblareau.bsky.social•16 days ago

After sequencing on Illumina devices, the T20 and 3' GEM-X yielded similar cells (~18,000), which we downsampled for comparison saturation analyses. Both informatics workflows were pretty painless to set up and run for saturation analyses /4

caleblareau.bsky.social•16 days ago

So what did we see? In brief, both technologies readily delineate the major cell types in PBMCs with clear expression of marker genes we've come to know and love. So at birdseye view, cell state separation and annotation is pretty consistent between technologies. /5

caleblareau.bsky.social•16 days ago

Labeling cell types with azimuth, the correspondence between the technologies at this resolution is pretty clear. I dug into the slight differences between the two and couldn't find anything that I trusted (e.g., overclustering / cell quality / mislabels explained anything off the y=x line here) /6

caleblareau.bsky.social•16 days ago

The differences start to emerge when you look a bit more closely. In terms of molecular complexity, PIPseq performed pretty well, but there's a pretty massive difference compared to GEM-X. At similar cell numbers and sequencing depth, we get about 63% more UMIs / cell with GEM-X over PIPseq V /7

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