biostudies-arrayexpressVincent GardeuxMus musculusSeuratCellrangerhttps://www.ebi.ac.uk/biostudies/studies/E-MTAB-13010Reprogramming approaches often produce heterogeneous cell fates and the mechanisms behind this heterogeneity are not well-understood. To address this gap, we developed scTF-seq, a technique inducing single-cell barcoded and doxycycline-inducible transcription factor (TF) overexpression while quantifying TF dose-dependent transcriptomic changes. Applied to mouse embryonic multipotent stromal cells (MSCs), scTF-seq produced a gain-of-function atlas for 384 murine TFs. This atlas offers a valuable resource for gene regulation and reprogramming research, identifying key TFs governing MSC lineage differentiation, cell cycle control, and their interplay. Leveraging the single-cell resolution, we dissected reprogramming heterogeneity along dose. We thereby revealed TF dose-dependent and stochastic cell state transitions, unveiling gene expression signatures that enhance our understanding and prediction of reprogramming efficiency. By exploring the relationship between TF dose and function, scTF-seq also allowed us to classify TFs into three sensitivity classes: low- versus high-capacity TFs with the latter split into ‘low’ and ‘high’ dose-sensitive groups. Finally, in combinatorial scTF-seq, we observed that the same TF can exhibit both synergistic and antagonistic effects on another TF depending on its dose. In summary, scTF-seq provides a powerful tool for gaining mechanistic insights into how TFs determine cell states, while offering valuable perspectives for cellular engineering strategies. For analysis and more details about this data, you can check our GitHub: https://github.com/DeplanckeLab/TF-seqbiostudies-arrayexpressGrowth Protocol - Both HEK293T and C3H10T1/2 cells were maintained in basic culture medium (containing high-glucose DMEM with GlutaMax and pyruvate, 10% FBS and 1x Penicillin-Streptomycin). All cells were placed at 37 °C and 5% CO2 in a humidified incubator.Sample Collection - Prior to use, cells were washed with PBS, dissociated with Trypsin-EDTA (0.05% for HEK293T and 0.25% for C3H10T1/2), resuspended with basic culture medium, filtered using 40 µm strainers and counted with Trypan blue live-dead stain using a Countess cell counter.Sequencing - Illumina NextSeq 500/Hiseq 4000 platform using the dual-index configuration following manufacturer’s instructionsLibrary Construction - Using Chromium Single Cell Expression 3’ Reagent KitsNucleic Acid Extraction - Using Chromium Single Cell Expression 3’ Reagent KitsOrganizationMINSEQE ScoreAssays and DataProcessed DataMAGE-TAB FilesSequence Alignment - All libraries were aligned using cellranger v3.0.2 on the GRCm38 (mm10, Ensembl release 96) genome, supplemented with the Vector DNA construct.Data Transformation - All libraries were processed following a pipeline described on Github: https://github.com/DeplanckeLab/TF-seqMetabolomicsUnknownTranscriptomicsGenomicsProteomicsIllumina HiSeq 4000RNA-seq of coding RNA from single cellsMus musculusAntoni GralakBart DeplanckeWanze ChenWangjie LiuMarjan BiočaninPernille RainderVincent GardeuxWouter SaelensfalseDissecting the impact of transcription factor dose on cell reprogramming heterogeneity using scTF-seqReprogramming approaches often produce heterogeneous cell fates and the mechanisms behind this heterogeneity are not well-understood. To address this gap, we developed scTF-seq, a technique inducing single-cell barcoded and doxycycline-inducible transcription factor (TF) overexpression while quantifying TF dose-dependent transcriptomic changes. Applied to mouse embryonic multipotent stromal cells (MSCs), scTF-seq produced a gain-of-function atlas for 384 murine TFs. This atlas offers a valuable resource for gene regulation and reprogramming research, identifying key TFs governing MSC lineage differentiation, cell cycle control, and their interplay. Leveraging the single-cell resolution, we dissected reprogramming heterogeneity along dose. We thereby revealed TF dose-dependent and stochastic cell state transitions, unveiling gene expression signatures that enhance our understanding and prediction of reprogramming efficiency. By exploring the relationship between TF dose and function, scTF-seq also allowed us to classify TFs into three sensitivity classes: low- versus high-capacity TFs with the latter split into ‘low’ and ‘high’ dose-sensitive groups. Finally, in combinatorial scTF-seq, we observed that the same TF can exhibit both synergistic and antagonistic effects on another TF depending on its dose. In summary, scTF-seq provides a powerful tool for gaining mechanistic insights into how TFs determine cell states, while offering valuable perspectives for cellular engineering strategies. For analysis and more details about this data, you can check our GitHub: https://github.com/DeplanckeLab/TF-seq2025-05-24T00:00:00Z2025-06-03T08:19:42.39Z2023-05-24T14:53:05.341ZE-MTAB-13010ERP155321EFO_0002944EFO_0004170EFO_0003789EFO_0005684EFO_0004917EFO_0005518EFO_0003816EFO_0004184