Single-cell transcriptional dynamics in a living vertebrate

Eck, Elizabeth; Moretti, Bruno; Schlomann, Brandon H.; Bragantini, Jordão; Lange, Merlin; Zhao, Xiang; VijayKumar, Shruthi; Valentin, Guillaume; Loureiro, Cristina; Soroldoni, Daniele; Royer, Loïc A.; Oates, Andrew C.; Garcia, Hernan G.

doi:10.1101/2024.01.03.574108

Cited by 7 publications

(2 citation statements)

References 59 publications

(98 reference statements)

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“…In contrast, in short wavelength patterns mobility increases phase variance due to steep phase gradients. Recent imaging of mRNA and protein cycles at single cell level in the embryo [14,16] may open the road to test these predictions.…”

Section: Discussionmentioning

confidence: 99%

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Statistical description of mobile oscillators in embryonic pattern formation

Uriu,

Morelli

2024

Preprint

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Synchronization of mobile oscillators occurs in numerous contexts, including physical, chemical, biological and engineered systems. In vertebrate embryonic development, a segmental body structure is generated by a population of mobile oscillators. Cells in this population produce autonomous gene expression rhythms, and interact with their neighbors through local signaling. These cells form an extended tissue where frequency and cell mobility gradients coexist. Gene expression kinematic waves travel through this tissue and pattern the segment boundaries. It has been shown that oscillator mobility promotes global synchronization. However, in vertebrate segment formation, mobility may also introduce local fluctuations in kinematic waves and impair segment boundaries. Here we derive a general framework for mobile oscillators that relates local mobility fluctuations to synchronization dynamics and pattern robustness. We formulate a statistical description of mobile phase oscillators in terms of a probability density. We obtain and solve diffusion equations for the average phase and variance, revealing the relationship between local fluctuations and global synchronization in a homogeneous population of oscillators. Analysis of the probability density for large mobility identifies a mean-field transition, where locally coupled oscillators start behaving as if each oscillator was coupled with all the others. We extend the statistical description to inhomogeneous systems to address the gradients present in the vertebrate segmenting tissue. The theory relates pattern stability to mobility, coupling and pattern wavelength. The general approach of the statistical description may be applied to mobile oscillators in other contexts, as well as to other patterning systems where mobility is present.

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Section: Discussionmentioning

confidence: 99%

“…1B) [13,14]. The difference in oscil-lation frequency results in traveling phase waves across the anterior-posterior axis of the segmenting tissue, as revealed by protein [15] and mRNA expression [16]. A new segment boundary is determined when a wave comes to the anterior part of the tissue [17,18].…”

Section: Introductionmentioning

confidence: 99%

Statistical description of mobile oscillators in embryonic pattern formation

Uriu,

Morelli

2024

Preprint

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Optogenetic dissection of transcriptional repression in a multicellular organism

Zhao,

Lammers,

Alamos

et al. 2024

Nat Commun

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Transcriptional control is fundamental to cellular function. However, despite knowing that transcription factors can repress or activate specific genes, how these functions are implemented at the molecular level has remained elusive, particularly in the endogenous context of developing animals. Here, we combine optogenetics, single-cell live-imaging, and mathematical modeling to study how a zinc-finger repressor, Knirps, induces switch-like transitions into long-lived quiescent states. Using optogenetics, we demonstrate that repression is rapidly reversible (~1 min) and memoryless. Furthermore, we show that the repressor acts by decreasing the frequency of transcriptional bursts in a manner consistent with an equilibrium binding model. Our results provide a quantitative framework for dissecting the in vivo biochemistry of eukaryotic transcriptional regulation.

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Factorization-based Imputation of Expression in Single-cell Transcriptomic Analysis (FIESTA) recovers Gene-Cell-State relationships

Mehrabad

Bhaskara

Spike

2021

Preprint

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MotivationSingle cell RNA sequencing (scRNA-seq) is a powerful gene expression profiling technique that is presently revolutionizing the study of complex cellular systems in the biological sciences. Existing single-cell RNA-sequencing methods suffer from sub-optimal target recovery leading to inaccurate measurements including many false negatives. The resulting ‘zero-inflated’ data may confound data interpretation and visualization.ResultsSince cells have coherent phenotypes defined by conserved molecular circuitries (i.e. multiple gene products working together) and since similar cells utilize similar circuits, information about each each expression value or ‘node’ in a multi-cell, multi-gene scRNA-Seq data set is expected to also be predictable from other nodes in the data set. Based on this logic, several approaches have been proposed to impute missing values by extracting information from non-zero measurements in a data set. In this study, we applied non-negative matrix factorization approaches to a selection of published scRNASeq data sets to recommend new values where original measurements are likely to be inaccurate and where ‘zero’ measurements are predicted to be false negatives. The resulting imputed data model predicts novel cell type markers and expression patterns more closely matching gene expression values from orthogonal measurements and/or predicted literature than the values obtained from other previously published imputation approaches.Contactbenjamin.spike@hci.utah.eduAvailability and implementationFIESTA is written in R and is available at https://github.com/elnazmirzaei/FIESTA and https://github.com/TheSpikeLab/FIESTA.

show abstract

Single-cell transcriptional dynamics in a living vertebrate

Cited by 7 publications

References 59 publications

Statistical description of mobile oscillators in embryonic pattern formation

Statistical description of mobile oscillators in embryonic pattern formation

Optogenetic dissection of transcriptional repression in a multicellular organism

Factorization-based Imputation of Expression in Single-cell Transcriptomic Analysis (FIESTA) recovers Gene-Cell-State relationships

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