Evidence map›Paper›PMID 36708705›Full record

ArticleCell2023

Bacterial droplet-based single-cell RNA-seq reveals antibiotic-associated heterogeneous cellular states.

Peijun Ma, Haley M Amemiya, Lorrie L He, Shivam J Gandhi, Robert Nicol, Roby P Bhattacharyya, Christopher S Smillie, Deborah T Hung

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 160 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
160citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

160 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. Siderophore sharing protects clonalApplied and environmental microbiology · 2026
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100 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Peijun MaThe Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA; Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA; Department of Genetics, Harvard Medical School, Boston, MA, USA.
Haley M AmemiyaThe Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA; Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA; Department of Genetics, Harvard Medical School, Boston, MA, USA.
Lorrie L HeThe Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Shivam J GandhiThe Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
Robert NicolThe Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Roby P BhattacharyyaThe Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA.
Christopher S SmillieThe Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
Deborah T HungThe Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA; Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA; Department of Genetics, Harvard Medical School, Boston, MA, USA. Electronic address: hung@molbio.mgh.harvard.edu.

Funding

Pilot & Feasibility ProgramP30DK043351 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI RAYMOND T CHUNG · 1991 to 2026
$35.3M
RNA based diagnostics for rapid pathogen identification and drug resistanceR01AI117043 · NIAID · BROAD INSTITUTE, INC. · PI HUNG, DEBORAH T · 2015 to 2019
$6.5M
NIAID NIH HHS R01 AI117043NIDDK NIH HHS P30 DK043351
6 · The paper itself

Abstract

We introduce BacDrop, a highly scalable technology for bacterial single-cell RNA sequencing that has overcome many challenges hindering the development of scRNA-seq in bacteria. BacDrop can be applied to thousands to millions of cells from both gram-negative and gram-positive species. It features universal ribosomal RNA depletion and combinatorial barcodes that enable multiplexing and massively parallel sequencing. We applied BacDrop to study Klebsiella pneumoniae clinical isolates and to elucidate their heterogeneous responses to antibiotic stress. In an unperturbed population presumed to be homogeneous, we found within-population heterogeneity largely driven by the expression of mobile genetic elements that promote the evolution of antibiotic resistance. Under antibiotic perturbation, BacDrop revealed transcriptionally distinct subpopulations associated with different phenotypic outcomes including antibiotic persistence. BacDrop thus can capture cellular states that cannot be detected by bulk RNA-seq, which will unlock new microbiological insights into bacterial responses to perturbations and larger bacterial communities such as the microbiome.

Indexed as

Gene Expression ProfilingSingle-Cell Gene Expression AnalysisBacteriaRNA-SeqSequence Analysis, RNASingle-Cell Analysisantibiotic persistenceantibiotic perturbationantibiotic resistancebacterial heterogeneitybacterial single-cell RNA-seqdropletmassively parallel sequencing

Identifiers

PMID36708705
PMCPMC10014032

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.