Assessing Markovian and Delay Models for Single-Nucleus RNA Sequencing

Gorin, Gennady; Yoshida, Shawn; Pachter, Lior

doi:10.1007/s11538-023-01213-9

Cited by 5 publications

(7 citation statements)

References 88 publications

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“…We found no evidence that nuclear export or splicing can be described by an effective reaction step with a fixed time delay -rather these seem to be described by one rate-limiting step with first-order kinetics. These results are broadly in agreement with a recent model-based analysis of single-cell and single-nucleus RNA sequencing data [62]. Importantly, our analysis shows that failing to account for post-transcriptional processing will lead to an overestimation of the number of gene states.…”

Section: Discussionsupporting

confidence: 92%

Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression

Nicoll,

Szavits-Nossan,

Evans

et al. 2023

Preprint

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What features of transcription can be learnt by fitting mathematical models of gene expression to mRNA count data? Given a suite of models, fitting to data selects an optimal one, thus identifying a probable transcriptional mechanism. Whilst attractive, the utility of this methodology remains unclear. Here, we sample steady-state, single-cell mRNA count distributions from parameters in the physiological range, and show they cannot be used to confidently estimate the number of inactive gene states, i.e. the number of rate-limiting steps in transcriptional initiation. Distributions from over 99% of the parameter space generated using models with 2, 3, or 4 inactive states can be well fit by one with a single inactive state. However, we show that if the mRNA lifetime is hours long, then for many minutes following induction, the increase in the mean mRNA count obeys a power law whose exponent equals the sum of the number of states visited from the initial inactive state to the active state and the number of rate-limiting post-transcriptional processing steps. Our study shows that non-linear regression estimation of the exponent from eukaryotic data is sufficient to estimate the total number of regulatory steps in transcription initiation, splicing, and nuclear export.

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Section: Discussionsupporting

confidence: 92%

Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression

Nicoll,

Szavits-Nossan,

Evans

et al. 2023

Preprint

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“…In conclusion, by quantifying sense intragenic scRNA-seq reads according to their splicing status and performing clustering analysis and differential expression analysis on them and their combinations, we found that their cluster assignments slightly diverge but still show consistency and well-known cell type markers are discovered exclusively from each modality. Together with the previous finding that unspliced counts show gene length bias [Phipson et al, 2017, 10x, 2021, Chamberlin et al, 2022, Gorin et al, 2023], our results emphasize the necessity of improving existing methods and developing new algorithms to consider the signals from different splicing statuses jointly, but separately, for more comprehensive analysis results.…”

Section: Resultssupporting

confidence: 79%

“…We note, however, that we have adopted in this manuscript a rather conservative notion of spliced and unspliced status [Eldj’sarn Hjörleifsson et al, 2022, He et al, 2023], and other approaches, that consider purely exonic reads as arising from spliced RNA, will lead to different ratios. Existing studies have shown the prevalence of intronic reads [He et al, 2023, Pool et al, 2023, Chamberlin et al, 2022, 10x, 2021] and proposed algorithms that utilize unspliced reads in different ways [La Manno et al, 2018, 10x, 2022b, Gorin et al, 2023].…”

Section: Resultsmentioning

confidence: 99%

“…In general, throughout this article, we will refer to “unspliced” molecules in the understanding that they may be actively undergoing splicing and hence be partially spliced. Meanwhile, researchers have realized the underappreciated value of off-target reads and designed sophisticated algorithms to incorporate off-target reads in scRNA-seq data analysis [Chamberlin et al, 2022, 10x, 2022b, Pool et al, 2023, Chari et al, 2023, Gorin et al, 2023]. One example is single-cell RNA velocity, in which cellular splicing dynamics are inferred using spliced gene counts from reads compatible with spliced transcripts, and unspliced counts accounting for reads compatible with unspliced transripts [La Manno et al, 2018].…”

Section: Introductionmentioning

confidence: 99%

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scCensus: Off-target scRNA-seq reads reveal meaningful biology

He,

Mount,

Patro

2024

Preprint

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Single-cell RNA-sequencing (scRNA-seq) provides unprecedented insights into cellular heterogeneity. Although scRNA-seq reads from most prevalent and popular tagged-end protocols are expected to arise from the 3′ end of polyadenylated RNAs, recent studies have shown that “off-target” reads can constitute a substantial portion of the read population. in this work, we introducedscCensus, a comprehensive analysis workflow for systematically evaluating and categorizing off-target reads in scRNA-seq. We appliedscCensusto seven scRNA-seq datasets. Our analysis of intergenic reads shows that these off-target reads contain information about chromatin structure and can be used to identify similar cells across modalities. Our analysis of antisense reads suggests that these reads can be used to improve gene detection and capture interesting transcriptional activities like antisense transcription. Furthermore, using splice-aware quantification, we find that spliced and unspliced reads provide distinct information about cell clusters and biomarkers, suggesting the utility of integrating signals from reads with different splicing statuses.Overall, our results suggest that off-target scRNA-seq reads contain underappreciated information about various transcriptional activities. These observations about yet-unexploited information in existing scRNA-seq data will help guide and motivate the community to improve current algorithms and analysis methods, and to develop novel approaches that utilize off-target reads to extend the reach and accuracy of single-cell data analysis pipelines.Data AvailabilityThe scripts for reproducing the results are available athttps://github.com/COMBINE-lab/sc-census. Supplementary files can be found athttps://doi.org/10.5281/zenodo.10520670.

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“…Models involving more than one type of mRNA, i.e. describing all or a subset of nascent, mature nuclear and mature cytoplasmic mRNA levels, have been constructed and solved either numerically [15,19] or analytically for various special cases of interest [20][21][22][23][24][25][26][27]. The analytical solutions provide expressions for the moments or the marginal distributions of mRNA levels except Ref [20] which additionally provides the joint distribution of nuclear and cytoplasmic mRNA under the assumption of instantaneous transcriptional bursting, i.e.…”

mentioning

confidence: 99%

Joint distribution of nuclear and cytoplasmic mRNA levels in stochastic models of gene expression: analytical results and parameter inference

Wang,

Szavits-Nossan,

Cao

et al. 2024

Preprint

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Stochastic models of gene expression are routinely used to explain large variability in measured mRNA levels between cells. These models typically predict the distribution of the total mRNA level per cell but ignore compartment-specific measurements which are becoming increasingly common. Here we construct a two-compartment model that describes promoter switching between active and inactive states, transcription of nuclear mRNA and its export to the cytoplasm where it decays. We obtain an analytical solution for the joint distribution of nuclear and cytoplasmic mRNA levels in steady-state conditions. Based on this solution, we build an efficient and accurate parameter inference method which is orders of magnitude faster than conventional methods.

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Assessing Markovian and Delay Models for Single-Nucleus RNA Sequencing

Cited by 5 publications

References 88 publications

Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression

Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression

scCensus: Off-target scRNA-seq reads reveal meaningful biology

Joint distribution of nuclear and cytoplasmic mRNA levels in stochastic models of gene expression: analytical results and parameter inference

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