SLAM-ITseq: Sequencing cell type-specific transcriptomes without cell sorting

Matsushima, Wayo; Herzog, Veronika A.; Neumann, Tobias; Gapp, Katharina; Zuber, Johannes; Ameres, Stefan L.; Miska, Eric A.

doi:10.1242/dev.164640

Cited by 33 publications

(35 citation statements)

References 24 publications

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“…A reduced conversion rate is likely to worsen sensitivity. Finally, methods based on RNA metabolic labeling cannot be readily applied to model organisms, mammals (Matsushima et al 2018) or plants (Sidaway-Lee et al 2014), in vivo.…”

Section: Experimental and Computational Pitfalls Of Rna Metabolic Labmentioning

confidence: 99%

“…Despite their advantages and popularity, methods based on RNA metabolic labeling are affected by various pitfalls, especially when a limited amount of nascent RNA is produced and when aiming at studying very short responses (Baptista and Dölken 2018). Moreover, these methods cannot be readily applied to model organisms, be it mammals (Matsushima et al 2018) or plants (Sidaway-Lee et al 2014), in vivo. For all these reasons, being able to study RNA dynamics from just total RNA would be a valuable alternative.…”

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confidence: 99%

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Genome-wide dynamics of RNA synthesis, processing, and degradation without RNA metabolic labeling

et al. 2020

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The quantification of the kinetic rates of RNA synthesis, processing, and degradation are largely based on the integrative analysis of total and nascent transcription, the latter being quantified through RNA metabolic labeling. We developed INSPEcT−, a computational method based on the mathematical modeling of premature and mature RNA expression that is able to quantify kinetic rates from steady-state or time course total RNA-seq data without requiring any information on nascent transcripts. Our approach outperforms available solutions, closely recapitulates the kinetic rates obtained through RNA metabolic labeling, improves the ability to detect changes in transcript half-lives, reduces the cost and complexity of the experiments, and can be adopted to study experimental conditions in which nascent transcription cannot be readily profiled. Finally, we applied INSPEcT− to the characterization of post-transcriptional regulation landscapes in dozens of physiological and disease conditions. This approach was included in the INSPEcT Bioconductor package, which can now unveil RNA dynamics from steady-state or time course data, with or without the profiling of nascent RNA.

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Section: Experimental and Computational Pitfalls Of Rna Metabolic Labmentioning

confidence: 99%

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confidence: 99%

Genome-wide dynamics of RNA synthesis, processing, and degradation without RNA metabolic labeling

et al. 2020

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“…I envision that the combination of multiple autochthonous models in parallel and scRNA-seq and BS-seq may be exploited to evaluate the way the core tumor and in the infiltrating margins respond to individual treatments. Importantly, metabolic labeling of RNA in vivo now enables identifying faster and more accurately the adaptive transcriptional changes ( 173 ).…”

Section: Experimental Models For High-grade Gliomasmentioning

confidence: 99%

Next-Generation in vivo Modeling of Human Cancers

Gargiulo

2018

Front. Oncol.

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Animal models of human cancers played a major role in our current understanding of tumor biology. In pre-clinical oncology, animal models empowered drug target and biomarker discovery and validation. In turn, this resulted in improved care for cancer patients. In the quest for understanding and treating a diverse spectrum of cancer types, technological breakthroughs in genetic engineering and single cell “omics” offer tremendous potential to enhance the informative value of pre-clinical models. Here, I review the state-of-the-art in modeling human cancers with focus on animal models for human malignant gliomas. The review highlights the use of glioma models in dissecting mechanisms of tumor initiation, in the retrospective identification of tumor cell-of-origin, in understanding tumor heterogeneity and in testing the potential of immuno-oncology. I build on the deep review of glioma models as a basis for a more general discussion of the potential ways in which transformative technologies may shape the next-generation of pre-clinical models. I argue that refining animal models along the proposed lines will benefit the success rate of translation for pre-clinical research in oncology.

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“…A major limitation of sci-fate is that S4U labeling experiments are generally performed in vitro. 33 However, recent studies have shown that S4U can be used in conjunction with transgenic UPRT- 34 expressing mice to stably label cell type-specific nascent RNA transcription in vivo [62][63][64] , suggesting that All cell lines (A549, HEK293T and NIH/3T3 cells) were trypsinized, spun down at 300xg for 5 min (4°C) 12 and washed once in 1X ice-cold PBS. All cells were fixed with 4ml ice cold 4% paraformaldehyde (EMS) 13 for 15 min on ice.…”

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Characterizing the temporal dynamics of gene expression in single cells with sci-fate

Cao

Zhou

Steemers

et al. 2019

Preprint

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2Gene expression programs are dynamic, e.g. the cell cycle, response to stimuli, normal differentiation and 3 development, etc. However, nearly all techniques for profiling gene expression in single cells fail to 4 directly capture the dynamics of transcriptional programs, which limits the scope of biology that can be 5 effectively investigated. Towards addressing this, we developed sci-fate, a new technique that combines 6 S4U labeling of newly synthesized mRNA with single cell combinatorial indexing (sci-), in order to 7 concurrently profile the whole and newly synthesized transcriptome in each of many single cells. As a 8 proof-of-concept, we applied sci-fate to a model system of cortisol response and characterized expression 9 dynamics in over 6,000 single cells. From these data, we quantify the dynamics of the cell cycle and 10 glucocorticoid receptor activation, while also exploring their intersection. We furthermore use these data 11 to develop a framework for inferring the distribution of cell state transitions. We anticipate sci-fate will 12 be broadly applicable to quantitatively characterize transcriptional dynamics in diverse systems. 13 Main 1 2During organismal development, as well as during myriad physiological and pathophysiological 3 processes, individual cells traverse a manifold of molecularly and functionally distinct states. The accurate 4 characterization of these trajectories is key to advancing our understanding of each such process, for 5 identifying the causal factors that drive them, and for rationally designing effective perturbations of them. 6 However, although experimental methods for profiling various aspects of single cell biology have recently 7 proliferated, nearly all such methods deliver only a static snapshot of each cell, e.g. of gene expression at 8 the moment of fixation. 9 10To recover temporal dynamics, several groups have developed computational methods that place 11 individual cells along a continuous trajectory based on single cell RNA-seq data, i.e. the concept of 12 pseudotime 1-6 . However, such methods are inherently limited in several important ways, including that 13 they are inferring rather than directly measuring dynamics, that they are dependent on sufficient 14 representation across the trajectory, and that they may fail to capture the detailed dynamics of individual 15 cells (e.g. directionality, multiple superimposed potentials, etc.) 7 . Although time-lapse microscopy is a 16 distinct technology that overcomes some of these limitations, it is limited in throughput and scope (e.g. 17enabling visualization of a few marker genes in a few cells), and as such may be insufficient to decipher 18 the complexity of many biological systems. 20Here we describe a novel technique, sci-fate, to measure the dynamics of gene expression in single cells 21 at the level of the whole transcriptome. In brief, we integrated protocols for labeling newly synthesized 22 mRNA with 4-thiouridine (S4U) 8,9 with single cell combinatorial indexing RNA-seq (sci-RNA-seq 10 ). As 23 a p...

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SLAM-ITseq: Sequencing cell type-specific transcriptomes without cell sorting

Cited by 33 publications

References 24 publications

Genome-wide dynamics of RNA synthesis, processing, and degradation without RNA metabolic labeling

Genome-wide dynamics of RNA synthesis, processing, and degradation without RNA metabolic labeling

Next-Generation in vivo Modeling of Human Cancers

Characterizing the temporal dynamics of gene expression in single cells with sci-fate

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