Deconvolving Stochastic Transcriptional Profiles for Single-Cell Heterogeneity
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Solution Overview
Problem
Current methods for inferring single-cell gene expression characteristics from small groups of cells are limited by technical variations in RNA extraction and handling, leading to unreliable and biased results, especially when discovering novel regulatory states without predefined networks, and often require large numbers of cells for accurate profiling.
Innovation Solution
A computational method using maximum-likelihood inference and mixture models to reconstruct single-cell gene expression characteristics from small groups of cells, approximating regulatory states with lognormal and exponential distributions, allowing for the inference of single-cell gene expression even with limited sample sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-cell profiling methods are used to monitor regulatory states, then cell-to-cell heterogeneities can be detected, but technical variations in RNA extraction and handling introduce noise that cannot be separated from biological variation without profiling many cells
Solution Approach 1:
The patent segments the population-averaged data into distinct regulatory states using computational deconvolution methods. By dividing the mixed cellular states into separable components through mathematical decomposition, the method recovers single-cell expression characteristics without actually measuring individual cells, thus avoiding technical noise from single-cell handling while still detecting heterogeneities.
Solution Approach 2:
The patent introduces computational inference algorithms as an intermediary between population-averaged measurements and single-cell expression characteristics. This computational mediator deconvolves the mixed states and recovers underlying single-cell profiles, eliminating the need for direct single-cell measurement and its associated technical variations.
2Adaptability or versatility
If population-averaged data is analyzed to define regulatory signatures, then computational deconvolution can identify discrete subpopulations, but hundreds of coexpressed markers or calibration with purified cell populations are required
Solution Approach 1:
The patent extracts single-cell expression characteristics directly from population-averaged data through computational deconvolution, removing the need for extensive marker panels or purified cell population calibrations. The method isolates and recovers the underlying single-cell profiles that generate the observed population averages, simplifying the experimental requirements while maintaining the ability to discover novel regulatory states.
3Reliability
If stochastic-profiling experiments are used to identify regulatory heterogeneities, then quantitative and highly reproducible results are obtained, but explicit information about single cells is lost in the averages
Solution Approach 1:
The patent applies feedback by using the observed population-averaged stochastic profiles to inform and constrain the computational deconvolution process. The method iteratively refines the inferred single-cell characteristics to ensure they reproduce the observed population averages, thereby recovering single-cell information while maintaining the reproducibility of heterogeneity identification.
Data Source
AI summary
Regulated changes in gene expression underlie many biological processes, but globally profiling cell-to-cell variations in transcriptional regulation is problematic when measuring single cells. Transcriptome-wide identification of regulatory heterogeneities can be robustly achieved by randomly collecting small numbers of cells followed by statistical analysis. However, this stochastic-profiling approach blurs out the expression states of the individual cells in each pooled sample. Various aspects of the disclosure show that the underlying distribution of single-cell regulatory states can be deconvolved from stochastic-profiling data through maximum-likelihood inference. Guided by the mechanisms of transcriptional regulation, the disclosure provides mixture models for cell-to-cell regulatory heterogeneity which result in likelihood functions to infer model parameters. Inferences that validate both computationally and experimentally different mixture models, which include regulatory states for multicellular function occupied by as few as one in 40 cells of the population, are also encompassed. When the disclosed method extends to programs of heterogeneously coexpressed transcripts, the population-level inferences are much more accurate with pooled samples than with one-cell samples when the extent of sampling was limited. The disclosed deconvolution method provides a means to quantify the heterogeneous regulation of molecular states efficiently and gain a deeper understanding of the heterogeneous execution of cell decisions.