Evidence map›Paper›PMID 41332668›Full record

ArticlebioRxiv : the preprint server for biology2025

A unified genetic perturbation language for human cellular programming.

Austin Hartman, Oliver Takacsi-Nagy, Courtney Kernick, Nicole E Theberath, Johnathan Lu, Lujing Wu, Michelle Mantilla, Siddhesh Mittra, Alison McClellan, Nicole Johnson and 11 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

21 authors.

Austin HartmanDepartment of Pathology, Stanford University, Stanford, CA, USA.
Oliver Takacsi-NagyDepartment of Pathology, Stanford University, Stanford, CA, USA.
Courtney KernickDepartment of Pathology, Stanford University, Stanford, CA, USA.
Nicole E TheberathDepartment of Pathology, Stanford University, Stanford, CA, USA.
Johnathan LuDepartment of Pathology, Stanford University, Stanford, CA, USA.
Lujing WuDepartment of Pathology, Stanford University, Stanford, CA, USA.
Michelle MantillaDepartment of Pathology, Stanford University, Stanford, CA, USA.
Siddhesh MittraDepartment of Pathology, Stanford University, Stanford, CA, USA.
Alison McClellanDepartment of Pathology, Stanford University, Stanford, CA, USA.
Nicole JohnsonDepartment of Pathology, Stanford University, Stanford, CA, USA.
Lina MohamadDepartment of Pathology, Stanford University, Stanford, CA, USA.
Lesly Castillo-ColinDepartment of Pathology, Stanford University, Stanford, CA, USA.
Farzad HoqueDepartment of Pathology, Stanford University, Stanford, CA, USA.
Alexander EapenDepartment of Pathology, Stanford University, Stanford, CA, USA.
Andy ChenDepartment of Pathology, Stanford University, Stanford, CA, USA.
Laura M MoserDepartment of Pathology, Stanford University, Stanford, CA, USA.
Trini RogandoDepartment of Pathology, Stanford University, Stanford, CA, USA.
Anabella HernandezDepartment of Pathology, Stanford University, Stanford, CA, USA.
Katherine SantostefanoDepartment of Pathology, Stanford University, Stanford, CA, USA.
Ansuman T SatpathyDepartment of Pathology, Stanford University, Stanford, CA, USA.
Theodore L RothDepartment of Pathology, Stanford University, Stanford, CA, USA.ORCID 0000-0002-3970-9573

Funding

Synthetic Cell State Engineering for Primary Human Cellular TherapiesDP2CA311217 · NCI · STANFORD UNIVERSITY · PI ROTH, THEODORE LEE · 2025 to 2025
$1.4M
Dissecting intrinsic variability in engineered T Cell immunotherapiesK08CA286740 · NCI · STANFORD UNIVERSITY · PI Theodore Lee Roth · 2024 to 2026
$747k
NCI NIH HHS DP2 CA311217NCI NIH HHS K08 CA286740
6 · The paper itself

Abstract

Evolution simultaneously and combinatorially explores complex genetic changes across perturbation classes, including gene knockouts, knockdowns, overexpression, and the creation of new genes from existing domains. Separate technologies are capable of genetic perturbations at scale in human cells, but these methods are largely mutually incompatible. Here we present CRISPR-All, a unified genetic perturbation language for programming of any major type of genetic perturbation simultaneously, in any combination, at genome scale, in primary human cells. This is enabled by a standardized molecular architecture for each major perturbation class, development of a functional syntax for combining arbitrary numbers of elements across classes, and linkage to unique single cell compatible barcodes. To facilitate use, CRISPR-All converts high level descriptions of desired complex genetic changes into a single DNA sequence that can rewire genomic programs within a cell. Using the CRISPR-All language allowed for head-to-head functional comparisons across perturbation types in a comprehensive analysis of all previously identified genetic enhancements of human CAR-T cells. Combining CRISPR-All programs with single cell RNA sequencing revealed a greater diversity of phenotypic states, including improved functional performance, only accessible through distinct perturbation classes. Finally, CRISPR-All combinatorial genome scale screening of up to four distinct perturbations simultaneously revealed additive functional improvements in human T cells accessible only through iterative multiplexing of modifications across perturbation classes. CRISPR-All enables exploration of a combinatorial genetic perturbation space, which may be impactful for biological and clinical applications.

Identifiers

PMID41332668
PMCPMC12667873

What OpenQuestion holds

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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.