Evidence map›Paper›PMID 38886529›Full record

ReviewNature protocols2024

High-throughput single-cell transcriptomics of bacteria using combinatorial barcoding.

Karl D Gaisser, Sophie N Skloss, Leandra M Brettner, Luana Paleologu, Charles M Roco, Alexander B Rosenberg, Matthew Hirano, R William DePaolo, Georg Seelig, Anna Kuchina

Abstract readReview
In one paragraph

Review in Nature protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Microbial Omics.Progress in molecular and subcellular biology · 2026
    Review
  8. Review
  9. Microbiome Single Cell Atlases Generated with a Commercial Instrument.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  10. Review
  11. Article
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

10 authors.

Karl D Gaisser *Institute for Systems Biology, Seattle, WA, USA.ORCID 0009-0002-8035-5137
Sophie N Skloss *Institute for Systems Biology, Seattle, WA, USA.ORCID 0009-0008-1394-5763
Leandra M BrettnerBiodesign Institute Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ, USA.
Luana PaleologuDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.ORCID 0000-0002-0780-7822
Charles M RocoParse Biosciences, Seattle, WA, USA.
Alexander B RosenbergParse Biosciences, Seattle, WA, USA.
Matthew HiranoDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.
R William DePaoloCenter for Microbiome Sciences and Therapeutics, School of Medicine, University of Washington, Seattle, WA, USA.
Georg SeeligDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.ORCID 0000-0002-3163-8782
Anna KuchinaInstitute for Systems Biology, Seattle, WA, USA. anna.kuchina@isbscience.org.ORCID 0000-0002-7267-101X

Funding

Single-cell transcriptomics of complex bacterial communitiesR35GM150994 · NIGMS · INSTITUTE FOR SYSTEMS BIOLOGY · PI Anna Kuchina · 2023 to 2026
$2.1M
Resolving Oral Bacteria Interactions with a High-Throughput Low-Cost Single-Cell Transcriptomics ApproachR21DE032890 · NIDCR · UNIVERSITY OF WASHINGTON · PI KUCHINA, ANNA, MCLEAN, JEFFREY SCOTT · 2023 to 2024
$485k
NIDCR NIH HHS R21 DE032890NIGMS NIH HHS R35 GM150994U.S. Department of Energy (DOE) DE-SC0023091U.S. Department of Health & Human Services | NIH | National Institute of Dental and Craniofacial Research (NIDCR) R21DE032890U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R35GM150994
6 · The paper itself

Abstract

Microbial split-pool ligation transcriptomics (microSPLiT) is a high-throughput single-cell RNA sequencing method for bacteria. With four combinatorial barcoding rounds, microSPLiT can profile transcriptional states in hundreds of thousands of Gram-negative and Gram-positive bacteria in a single experiment without specialized equipment. As bacterial samples are fixed and permeabilized before barcoding, they can be collected and stored ahead of time. During the first barcoding round, the fixed and permeabilized bacteria are distributed into a 96-well plate, where their transcripts are reverse transcribed into cDNA and labeled with the first well-specific barcode inside the cells. The cells are mixed and redistributed two more times into new 96-well plates, where the second and third barcodes are appended to the cDNA via in-cell ligation reactions. Finally, the cells are mixed and divided into aliquot sub-libraries, which can be stored until future use or prepared for sequencing with the addition of a fourth barcode. It takes 4 days to generate sequencing-ready libraries, including 1 day for collection and overnight fixation of samples. The standard plate setup enables single-cell transcriptional profiling of up to 1 million bacterial cells and up to 96 samples in a single barcoding experiment, with the possibility of expansion by adding barcoding rounds. The protocol requires experience in basic molecular biology techniques, handling of bacterial samples and preparation of DNA libraries for next-generation sequencing. It can be performed by experienced undergraduate or graduate students. Data analysis requires access to computing resources, familiarity with Unix command line and basic experience with Python or R.

Indexed as

High-Throughput Nucleotide SequencingSingle-Cell AnalysisBacteriaDNA Barcoding, TaxonomicGene Expression ProfilingGene LibraryTranscriptome

Identifiers

PMID38886529
PMCPMC11575931

What OpenQuestion holds

Textmetadata
LicenceTDM
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.