A single-cell resolved genotype-phenotype map using genome-wide genetic and environmental perturbations

Heterogeneity is inherent to living organisms and it determines cell fate and phenotypic variability. Despite its ubiquity, the underlying molecular mechanisms and the genetic basis linking genotype to-phenotype heterogeneity remain a central challenge. Here we construct a yeast knockout library with a clone and genotype RNA barcoding structure suitable for genome-scale analyses to generate a high-resolution single-cell yeast transcriptome atlas of 3500 mutants under control and stress conditions. We find that transcriptional heterogeneity reflects the coordinated expression of specific gene programs, generating a continuous of cell states that can be responsive to external insults. Cell state plasticity can be genetically modulated with mutants that act as state attractors and disruption of state homeostasis results in decreased adaptive fitness. Leveraging on intra-genetic variability, we establish that regulators of transcriptional heterogeneity are functionally diverse and influenced by the environment. Our multimodal perturbation-based single-cell Genotype-to-Transcriptome Atlas in yeast provides insights into organism-level responses.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2025-03-18
DOI
https://doi.org/10.1038/s41467-025-57600-4
Citations
2
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
1.61

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A single-cell resolved genotype-phenotype map using genome-wide genetic and environmental perturbations

Lars Michael Steinmetz, Yaima Matas, Mariona Nadal‐Ribelles, Francesc Posas et al.
2 citations
Nature Communications
Single-cell and spatial transcriptomics
1.61
article

A single-cell resolved genotype-phenotype map using genome-wide genetic and environmental perturbations

Lars Michael Steinmetz, Yaima Matas, Mariona Nadal‐Ribelles, Francesc Posas, Eulàlia de Nadal, Anna Díez-Villanueva, Camille Stephan‐Otto Attolini, Carme Solé
article en
2 citations

Abstract

Heterogeneity is inherent to living organisms and it determines cell fate and phenotypic variability. Despite its ubiquity, the underlying molecular mechanisms and the genetic basis linking genotype to-phenotype heterogeneity remain a central challenge. Here we construct a yeast knockout library with a clone and genotype RNA barcoding structure suitable for genome-scale analyses to generate a high-resolution single-cell yeast transcriptome atlas of 3500 mutants under control and stress conditions. We find that transcriptional heterogeneity reflects the coordinated expression of specific gene programs, generating a continuous of cell states that can be responsive to external insults. Cell state plasticity can be genetically modulated with mutants that act as state attractors and disruption of state homeostasis results in decreased adaptive fitness. Leveraging on intra-genetic variability, we establish that regulators of transcriptional heterogeneity are functionally diverse and influenced by the environment. Our multimodal perturbation-based single-cell Genotype-to-Transcriptome Atlas in yeast provides insights into organism-level responses.

Nature CommunicationsVol. 16(1)
Universitat Pompeu Fabra (ES), Institute for Research in Biomedicine (ES), European Molecular Biology Laboratory (DE), Stanford University (US)
Institute for Research in Biomedicine
Life in Land
Openalex Percentile: Top 19%
Single-cell and spatial transcriptomics
1.61
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.