Evidence map›Paper›PMID 36827376›Full record

ArticleScience advances2023

Microscale combinatorial stimulation of human myeloid cells reveals inflammatory priming by viral ligands.

Miguel Reyes, Samantha M Leff, Matteo Gentili, Nir Hacohen, Paul C Blainey

Abstract read
In one paragraph

Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Miguel ReyesBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0003-2776-0862
Samantha M LeffBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0002-6037-072X
Matteo GentiliBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0002-6970-8133
Nir HacohenBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0002-2349-2656
Paul C BlaineyBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0002-4889-8783

Funding

Single-cell genomic profiling to identify immune signatures of bacterial sepsis in humansR01AI153142 · NIAID · MASSACHUSETTS GENERAL HOSPITAL · PI FILBIN, MICHAEL, GOLDBERG, MARCIA B · 2021 to 2025
$5.0M
NIAID NIH HHS R01 AI153142
6 · The paper itself

Abstract

Cells sense a wide variety of signals and respond by adopting complex transcriptional states. Most single-cell profiling is carried out today at cellular baseline, blind to cells' potential spectrum of functional responses. Exploring the space of cellular responses experimentally requires access to a large combinatorial perturbation space. Single-cell genomics coupled with multiplexing techniques provide a useful tool for characterizing cell states across several experimental conditions. However, current multiplexing strategies require programmatic handling of many samples in macroscale arrayed formats, precluding their application in large-scale combinatorial analysis. Here, we introduce StimDrop, a method that combines antibody-based cell barcoding with parallel droplet processing to automatically formulate cell population × stimulus combinations in a microfluidic device. We applied StimDrop to profile the effects of 512 sequential stimulation conditions on human dendritic cells. Our results demonstrate that priming with viral ligands potentiates hyperinflammatory responses to a second stimulus, and show transcriptional signatures consistent with this phenomenon in myeloid cells of patients with severe COVID-19.

Indexed as

COVID-19HumansLab-On-A-Chip DevicesLigandsMyeloid CellsSingle-Cell AnalysisLigands

Identifiers

PMID36827376
PMCPMC9956118

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.