ArticleCell systems2025
Integration of multi-modal measurements identifies critical mechanisms of tuberculosis drug action.
Article in Cell systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbedScience advances · 2026Article
- Growth arrest of Mycobacterium tuberculosis in acidic environments enhances their survival of antibiotic treatment.PLoS biology · 2026Article
- Breaking the barrier: from biosynthetic inhibition to multidimensional modulation of the mycobacterial cell wall in tuberculosis therapy.Frontiers in pharmacology · 2026Review
- The eight pillars of within-host tuberculosis modelling.Frontiers in immunology · 2026Review
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Treatments for tuberculosis remain lengthy, motivating a search for new drugs with novel mechanisms of action. However, it remains challenging to determine the direct targets of a drug and which disrupted cellular processes lead to bacterial killing. We developed a computational tool, DECIPHAER (decoding cross-modal information of pharmacologies via autoencoders), to select the important correlated transcriptional and morphological responses of Mycobacterium tuberculosis to treatment. By finding a reduced feature space, DECIPHAER highlighted essential features of cellular damage. DECIPHAER provides cell-death-relevant insight into uni-modal datasets, enabling interrogation of drug treatment responses for which transcriptional data are unavailable. Using morphological data alone with DECIPHAER, we discovered that respiration inhibition by the polypharmacological drugs SQ109 and BM212 can influence cell death more than their effects on the cell wall. This study demonstrates that DECIPHAER can extract the critical shared information from multi-modal measurements to identify cell-death-relevant mechanisms of TB drugs. A record of this paper's transparent peer review process is included in the supplemental information.
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Registered trials
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