Evidence map›Paper›PMID 37790580›Full record

ArticleResearch square2023

Microbiome single cell atlases generated with a commercial instrument.

Adam Abate, Xiangpeng Li, Linfeng Xu, Benjamin Demaree, Cecilia Noecker, Jordan Bisanz, Daniel Weisgerber, Cyrus Modavi, Peter Turnbaugh

Open access · greenAbstract readPreprint
In one paragraph

Article in Research square, 2023. 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, 5 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Adam AbateUniversity of California, San Francisco.
Xiangpeng LiUniversity of California, San Francisco.ORCID 0000-0002-4230-5676
Linfeng XuUniversity of California, San Francisco.
Benjamin DemareeUniversity of California, San Francisco.ORCID 0000-0003-2278-6633
Cecilia NoeckerUniversity of California, San Francisco.
Jordan BisanzUniversity of California, San Francisco.
Daniel WeisgerberUniversity of California, San Francisco.
Cyrus ModaviUniversity of California, San Francisco.
Peter TurnbaughUniversity of California, San Francisco.ORCID 0000-0002-0888-2875
University of California, San Francisco · US

Funding

Predicting and preventing drug metabolism by the human gut microbiomeR01HL122593 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Peter James Turnbaugh · 2016 to 2026
$5.7M
Gigapixel digital PCR in Giant Unilamellar VesiclesR01EB019453 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ABATE, ADAM R., CHIU, CHARLES YEN · 2014 to 2022
$3.9M
Multi-omic dissection of the transcriptional, epigenetic, and proteomic signatures of cells infected with latent HIVR01AI149699 · NIAID · BRIGHAM AND WOMEN'S HOSPITAL · PI ABATE, ADAM R., QUINTANA, FRANCISCO J. · 2020 to 2024
$3.8M
Accurate and unbiased single cell sequencing with digital droplet MDAR01HG008978 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ABATE, ADAM R. · 2016 to 2018
$1.9M
A universal droplet microfluidic platform for ultrahigh-throughput biocatalyst evolutionR42GM143989 · NIGMS · FLUID DISCOVERY INC · PI ABATE, ADAM R., PRINCE, ROBIN · 2022 to 2023
$1.7M
Establishing the feasibility of editing the human gut microbiomeR01AT011117 · NCCIH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI TURNBAUGH, PETER JAMES · 2020 to 2023
$1.6M
SINGLE-CELL TRANSCRIPTOMIC ANALYSIS OF HIV-INFECTED CD4 T CELLS IN BLOOD AND LYMPHOID TISSUE USING PCR-ACTIVATED CELL SORTINGU01AI129206 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ABATE, ADAM R., BORITZ, ELI A · 2017 to 2020
$1.6M
Systems-level analysis of the metabolism and ecology of genetically intractable gut bacteriaF32GM140808 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NOECKER, CECILIA · 2021 to 2022
$134k
NCCIH NIH HHS R01 AT011117NHGRI NIH HHS R01 HG008978NHLBI NIH HHS R01 HL122593NIAID NIH HHS R01 AI149699NIAID NIH HHS U01 AI129206NIBIB NIH HHS R01 EB019453NIGMS NIH HHS F32 GM140808NIGMS NIH HHS R42 GM143989
6 · The paper itself

Abstract

Single cell sequencing is useful for resolving complex systems into their composite cell types and computationally mining them for unique features that are masked in pooled sequencing. However, while commercial instruments have made single cell analysis widespread for mammalian cells, analogous tools for microbes are limited. Here, we present EASi-seq (Easily Accessible Single microbe sequencing). By adapting the single cell workflow of the commercial Mission Bio Tapestri instrument, this method allows for efficient sequencing of individual microbes' genomes. EASi-seq allows thousands of microbes to be sequenced per run and, as we show, can generate detailed atlases of human and environmental microbiomes. The ability to capture large shotgun genome datasets from thousands of single microbes provides new opportunities in discovering and analyzing species subpopulations. To facilitate this, we develop a companion bioinformatic pipeline that clusters microbes by similarity, improving whole genome assembly, strain identification, taxonomic classification, and gene annotation. In addition, we demonstrate integration of metagenomic contigs with the EASi-seq datasets to reduce capture bias and increase coverage. Overall, EASi-seq enables high quality single cell genomic data for microbiome samples using an accessible workflow that can be run on a commercially available platform.

Identifiers

PMID37790580
PMCPMC10543498
OpenAlexW4386738826

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.