Evidence map›Paper›PMID 41847029›Full record

ArticlebioRxiv : the preprint server for biology2026

A Multispecies, Modality-Agnostic Scalable In Vivo Mosaic Screening Platform for Therapeutic Target Discovery.

Vishwaraj Sontake, Vinay Kartha, Neety Sahu, Daniel R Fuentes, Linda Chio, Hikaru Miyazaki, Jingshu Chen, Arnav Gupta, Jesi Nonora, Arthi Vaidyanathan and 21 more

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

31 authors.

Vishwaraj SontakeGordian Biotechnology, South San Francisco, CA USA.
Vinay KarthaGordian Biotechnology, South San Francisco, CA USA.
Neety SahuGordian Biotechnology, South San Francisco, CA USA.
Daniel R FuentesGordian Biotechnology, South San Francisco, CA USA.
Linda ChioGordian Biotechnology, South San Francisco, CA USA.
Hikaru MiyazakiGordian Biotechnology, South San Francisco, CA USA.
Jingshu ChenGordian Biotechnology, South San Francisco, CA USA.
Arnav GuptaGordian Biotechnology, South San Francisco, CA USA.
Jesi NonoraGordian Biotechnology, South San Francisco, CA USA.
Arthi VaidyanathanGordian Biotechnology, South San Francisco, CA USA.
Smitha ShambhuGordian Biotechnology, South San Francisco, CA USA.
Gayathri DonepudiGordian Biotechnology, South San Francisco, CA USA.
Carmen LeGordian Biotechnology, South San Francisco, CA USA.
Lianna FungGordian Biotechnology, South San Francisco, CA USA.
Amber LimGordian Biotechnology, South San Francisco, CA USA.
Chase BowmanGordian Biotechnology, South San Francisco, CA USA.
Diego GarciaGordian Biotechnology, South San Francisco, CA USA.
Dimitry PopovGordian Biotechnology, South San Francisco, CA USA.
Kelly FaganGordian Biotechnology, South San Francisco, CA USA.
Charlie LongtineGordian Biotechnology, South San Francisco, CA USA.
Juana M Cruz SampedroGordian Biotechnology, South San Francisco, CA USA.
Samuel B HaywardGordian Biotechnology, South San Francisco, CA USA.
Adam BiedrzyckiDepartment of Orthopaedic Surgery and Sports Medicine, University of Florida.
Barclay B PowellDepartment of Large Animal Clinical Sciences, UF.
Katie DoroschakGordian Biotechnology, South San Francisco, CA USA.
Rachael Watson LevingsDepartment of Orthopaedic Surgery and Sports Medicine, University of Florida.
Chris CarricoGordian Biotechnology, South San Francisco, CA USA.
Ian DriverGordian Biotechnology, South San Francisco, CA USA.
Chris TowneGordian Biotechnology, South San Francisco, CA USA.
Francisco LePortGordian Biotechnology, South San Francisco, CA USA.
Martin Borch JensenGordian Biotechnology, South San Francisco, CA USA.ORCID 0000-0002-8875-0345

Funding

In Vitro Toxicity Testing at Massive Scale in Diverse Primary Human CellsR44ES032515 · NIEHS · GORDIAN BIOTECHNOLOGY, INC. · PI BORCH JENSEN, MARTIN · 2021 to 2023
$1.6M
NIEHS NIH HHS R44 ES032515
6 · The paper itself

Abstract

Validating therapeutic targets for complex diseases requires investigating gene functions within native tissue architectures rather than reductionist in vitro models. Here we present a modality-agnostic AAV-based in vivo high-throughput screening platform capable of delivering knockouts, gain-of-function, and synthetic miRNA knockdowns directly to cells within the diseased environment. This system scales to hundreds of perturbations and is adaptable to diverse species and organ systems. To translate high-dimensional screen data into therapeutic assessment, we established a curated analysis framework that scores single-cell transcriptomes against human disease-specific molecular signatures. This method enables quantitative ranking of targets across distinct biological domains ranging from structural fibrosis to inflammatory signaling, to narrow in on the therapeutic potential of each intervention. We applied this strategy to screen loss- and gain-of-function libraries in a murine pulmonary fibrosis model and within the spontaneously osteoarthritic joints of aged horses, identifying metabolic, antifibrotic, and immunomodulatory targets. Importantly, our analysis framework successfully predicted functional outcomes in orthogonal human ex vivo tissue models, including soluble collagen reduction in lung slices and glycosaminoglycan restoration in cartilage, thus establishing a powerful paradigm for prioritizing therapeutic targets by uniting human disease signatures with highly multiplexed in vivo functional genomics.

Identifiers

PMID41847029
PMCPMC12991101

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.