Evidence map›Paper›PMID 42539225›Full record

ArticlebioRxiv : the preprint server for biology2026

Bacterial surface display enables lysis-independent joint host-pathogen single-cell profiling.

Cal Gunnarsson, Jeff C Hsiao, Jacob B Hochfelder, Sydney L Solomon, Bianca A Lepe, Sharon Zhu, Kelly Xu, Hattie A Chung, Bryan D Bryson

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

9 authors.

Cal GunnarssonDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Jeff C HsiaoDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Jacob B HochfelderDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Sydney L SolomonDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Bianca A LepeDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Sharon ZhuDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Kelly XuDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Hattie A ChungDepartment of Medicine, Cardiovascular Research Center, Yale School of Medicine, New Haven, CT, USA.ORCID 0000-0001-8417-5606
Bryan D BrysonDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0003-1716-6712

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heterogeneity in host-pathogen interactions arises from variation in both host cell state and pathogen state, yet most single-cell methods capture these features separately. Joint profiling is limited by fundamental technical mismatches between host- and pathogen-derived material, particularly differences in cell wall structure, lysis requirements, and molecular abundance. Here we introduce a lysis-independent strategy that enables unified measurement of intracellular bacterial presence and state alongside host single-cell profiles. We repurpose bacterial surface display to encode promoter activity and bacterial identity as antibody-detectable signals, rendering bacterial features compatible with existing antibody-based single-cell assays. This approach is modular across promoters, epitope tags, display scaffolds, and bacterial species, including

Identifiers

PMID42539225
PMCPMC13419783

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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.