Drug screens usually give either an image of what happened to cells or a molecular readout of which biology moved. ActiveSeq captures both from the same microwell. In this pilot the combined readout classified compounds better than either view on its own, across every member of the 35-compound control panel.
ActiveSeq reads an image and an RNA profile from the same microwell. Across 35 control compounds, image plus RNA classified held-out wells better than RNA alone or image alone. The practical implication: a Z-Screen hit arrives with both a phenotype and a molecular fingerprint, instead of the phenotype alone.
In early discovery, the fastest screens often leave teams asking what a hit actually means. Imaging scales well, but morphology alone is hard to translate into biology. RNA is more explanatory, but running it deeply across large libraries is usually too expensive.
ActiveSeq pairs the two readouts in a single well. A Z-Screen hit comes with an image of the cell response and an RNA fingerprint from the same small colony of cells. That does not remove every follow-up experiment, but it makes the first look at a program remarkable rich.
We analyzed two HEK293 pilot runs covering 20,222 matched wells. A 35-compound control panel tested whether the readout could recover known compound identity, and dummy-bead wells served as a built-in stress test on the assay chemistry.
For each well we compared the image signal, the RNA signal, and the combined signal, and asked four practical questions. Does the assay flag bad wells. Are the readouts reproducible. Do image and RNA organize the panel in related ways. Does combining them actually help on held-out wells.
We also compared the results to scGeneScope, a public treatment-matched imaging-plus-RNA benchmark on 28 control compounds. The experimental designs are different enough that this is not a head-to-head comparison - sGenescope profiled ~600,000 droplets, this dataset is from ~20,000 microwells - but ActiveSeq holds up pretty well even at an order of magnitude lower sampling.
Dummy-bead wells should not behave like real compound wells, and they did not. RNA yield collapsed exactly where it should have, so failed chemistry does not get to slip silently into the biology model.
Splitting wells into pseudo-replicates, both RNA and image features recovered compound identity far above chance. The signal also held across the two pilot runs, which is the basic reliability bar for a screening readout.
Image and RNA organized the compound panel in related ways: enough overlap to trust the biology, enough difference that combining them adds information.
On held-out wells the combined readout beat image alone and RNA alone. The gain was distributed across the panel, not driven by a handful of easy compounds.
scGeneScope is larger and built differently, so this is not a head-to-head omparison. After adjusting for task difficulty, the pilot's multimodal effect sits in the same range as this public reference.
ActiveSeq changes the first question after a screen. Teams stop asking only which wells looked different and start asking what the cells looked like and which molecular programs moved with them. The pilot demonstrates that this is feasible, reproducible, that the two views add orthogonal information.
Public release. The preprint PDF is hosted here; the canonical workspace and per-paper analysis scripts live on Zenodo with a citable DOI.