BayFish: Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells

Mariana Gómez-Schiavon,Nicolas E Buchler,Anne E West,Liang-Fu Chen

doi:10.1186/s13059-017-1297-9

Mariana Gómez-Schiavon, Nicolas E Buchler + Show 2 more

Open Access

https://doi.org/10.1186/s13059-017-1297-9

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Abstract

Single-molecule RNA fluorescence in situ hybridization (smFISH) provides unparalleled resolution in the measurement of the abundance and localization of nascent and mature RNA transcripts in fixed, single cells. We developed a computational pipeline (BayFish) to infer the kinetic parameters of gene expression from smFISH data at multiple time points after gene induction. Given an underlying model of gene expression, BayFish uses a Monte Carlo method to estimate the Bayesian posterior probability of the model parameters and quantify the parameter uncertainty given the observed smFISH data. We tested BayFish on synthetic data and smFISH measurements of the neuronal activity-inducible gene Npas4 in primary neurons.

Highlights

Cell-to-cell variation in gene expression across an isogenic population is a fact of life
The user specifies any mathematical model of stochastic gene expression with an unknown set of parameters (θ ) and provides Single-molecule RNA fluorescence in situ hybridization (smFISH) data (Y ) at different time points before and after induction
Each iteration of the Monte Carlo method uses several numerical subroutines to calculate the time evolution of the mRNA and active transcription site (TS) distributions given a set of model parameters (θ ), to evaluate the likelihood that the smFISH data (Y ) were sampled from this distribution or L = P(Y |θ), and to calculate the Bayesian posterior probability P = P(θ|Y ) given the likelihood and priors

Summary

Introduction

Cell-to-cell variation in gene expression across an isogenic population is a fact of life. The existence of multiple promoter states with different expression rates can generate transcriptional bursting, which are episodes of transcriptional activity followed by long periods of inactivity [2,3,4]. This phenomenon has been observed in bacteria [5, 6], yeast [7, 8], flies [9, 10], and mammals [11,12,13,14,15,16].

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