Evidence map›Paper›PMID 42067624›Full record

ReviewNature microbiology2026

Unravelling bacterial complexity at high resolution with single-cell transcriptomics.

Anne E Clatworthy, Vincenzo P DiNatale, Emanuel Burgos-Robles, Fernando Lopes, Chris Smillie, Deborah T Hung

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Anne E ClatworthyDepartment of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA.
Vincenzo P DiNataleDepartment of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA.
Emanuel Burgos-RoblesCenter for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA.
Fernando LopesDepartment of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA.
Chris SmillieCenter for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA. csmillie@mgh.harvard.edu.
Deborah T HungDepartment of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA. hung@molbio.mgh.harvard.edu.ORCID http://orcid.org/0000-0003-4262-0673

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Phenotypic heterogeneity, a feature of both bacteria and eukaryotic cells, arises from inherent cell-to-cell variability. In eukaryotes, single-cell RNA sequencing has led to an explosion in understanding how heterogeneity impacts different cell types and states in organs and tissues. While single-cell RNA sequencing analyses in bacteria have lagged behind eukaryotic studies, recent technological advances now enable similar, high-resolution studies to be performed at scale in bacteria, yielding fundamental insights into how heterogeneity influences bacterial physiology, metabolism, antibiotic resistance, pathogenesis and interactions within complex microbial communities. Here we review recent advances in bacterial single-cell RNA sequencing, including the methods developed so far and what has been learned from their application. We also discuss technological and computational challenges going forwards, the need for standardization and how that could be achieved, and how this emerging field is now poised to revolutionize our understanding of bacterial physiology, infection biology and interactions within bacterial communities, such as the microbiota.

Indexed as

BacteriaSingle-Cell AnalysisTranscriptomeMicrobiotaSequence Analysis, RNASingle-Cell Gene Expression Analysis

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.