High Throughput Yeast Strain Phenotyping with Droplet-Based RNA Sequencing
May 21st, 2020
6.8K views
A bottleneck in the ‘design-build-test’ cycle of microbial engineering is the speed at which we can perform functional screens of strains. We describe a high-throughput method for strain screening applied to hundreds to thousands of yeast cells per experiment that utilizes droplet-based RNA sequencing.
Overview
This study presents a high-throughput method for strain screening in microbial engineering, specifically utilizing droplet-based RNA sequencing to analyze yeast cells. The method allows for functional assessments of engineered microbes at a genome-wide scale.
Key Study Components
Area of Science
- Microbial Engineering
- High-Throughput Screening
- RNA Sequencing
Background
- Functional screening of microbial strains is often slow and inefficient.
- High-throughput methods can significantly enhance the speed of these assessments.
- Droplet-based RNA sequencing offers a novel approach to evaluate microbial functions.
- Understanding genome perturbations can inform microbial engineering strategies.
Purpose of Study
- To develop a rapid screening method for yeast strains using RNA sequencing.
- To elucidate the functional impacts of genomic changes in engineered microbes.
- To enhance the efficiency of the design-build-test cycle in microbial engineering.
Methods Used
- Yeast cells were cultured and prepared in a specific concentration.
- Droplet-based microfluidics were employed to encapsulate yeast cells.
- RNA sequencing was performed on the encapsulated cells to assess gene expression.
- Bioinformatic tools were applied to analyze the sequencing data.
Main Results
- The method successfully encapsulated yeast cells in droplets for high-throughput analysis.
- Isogenic colonies were generated and analyzed for gene expression patterns.
- Principal component analysis revealed distinct clusters of gene expression.
- Findings indicated significant differences in gene expression related to colony morphology.
Conclusions
- This high-throughput method enhances the capability to screen microbial strains efficiently.
- Insights gained can inform future engineering of microbes for biotechnological applications.
- The approach is applicable to various microbial species beyond yeast.
Frequently Asked Questions
What is the significance of high-throughput RNA sequencing?
High-throughput RNA sequencing allows for rapid and comprehensive analysis of gene expression across many samples, facilitating the study of microbial functions.
How does this method improve microbial engineering?
By enabling faster functional screening, this method accelerates the design-build-test cycle, allowing for more efficient development of engineered microbes.
What types of microbes can this method be applied to?
While this study focuses on yeast, the method can be adapted for use with various microbial species.
What are the potential applications of this research?
The findings can be applied to biotechnological developments, including the production of biologics for disease treatment.
What challenges does this method address in microbial engineering?
It addresses the slow pace of functional screening, enabling quicker assessments of engineered strains.
How does the encapsulation process work?
Yeast cells are encapsulated in microfluidic droplets, allowing for controlled environments for growth and analysis.
Executive Industry Relevance
High-throughput strain screening remains a bottleneck in microbial engineering despite advances in genome editing. This droplet-based RNA sequencing method enables genome-wide functional assessment of engineered yeast strains, directly addressing the trade-off between information output and throughput. By providing phenotypic inference from transcriptional profiles, it supports predictive confidence in strain selection and accelerates the design-build-test cycle for biologics production.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses by linking genomic modifications to functional phenotypes via transcriptome-wide readouts.
- Operational Value: Supports biological de-risking through quantitative gene expression profiling that clarifies pathway-level impacts of genetic edits.
- Predictive Value: Facilitates portfolio triage by identifying strains with desired expression signatures linked to target pathways, reducing reliance on low-throughput assays.
Screening & Assay Development
- Assay Readiness: Generates standardized, droplet-encapsulated isogenic yeast colonies suitable for reproducible RNA-seq based phenotypic screening.
- Scalability: Processes thousands of genetically distinct strains in parallel, overcoming limitations of traditional colony picking and array-based methods.
- Data Quality: Produces quantitative, multiplexed gene expression measurements enabling robust comparison across strain libraries.
Translational & Preclinical Research
- Disease Relevance: Applied to Candida albicans white-opaque switching, a virulence-related phenotype, demonstrating utility in pathogenic strain characterization.
- Translational Continuity: Links genetic modifications to phenotypic outcomes through expression biomarkers (e.g., WH11, STF2), supporting biomarker-aligned strain selection.
- Preclinical De-risking: Enables early identification of non-functional or aberrant strains, reducing failure risk in downstream development.
Pipeline & Workflow Integration
The method integrates into microbial strain engineering workflows from library construction to functional validation, enabling transcriptome-driven strain prioritization before lead identification.
- Discovery Biology: Supports hypothesis testing by revealing how genomic perturbations affect global gene expression in isogenic backgrounds.
- Screening: Delivers assay-ready, standardized microbial systems with reproducible transcriptional outputs for compound or condition response testing.
- Analytics: Provides PCA and tSNE-derived expression clusters and marker gene overlays that enable phenotypic classification and strain comparison.
- Translational Research: Connects strain genotypes to phenotypic states via conserved markers, supporting continuity from discovery to preclinical validation.
- Enterprise Reuse: Establishes a reusable platform for screening yeast and other microbial strain libraries across multiple projects and therapeutic areas.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence in strain-function relationships by reducing mechanistic ambiguity through genome-scale expression data.
- Operational Value: Enhances reproducibility and standardization via microfluidic encapsulation and controlled hydrogel-based colony growth.
- Strategic Value: Improves go/no-go decision efficiency and capital allocation by enabling rapid, multi-parameter strain evaluation.
- Portfolio Impact: Supports risk-adjusted advancement by identifying strains with molecular profiles indicative of desired phenotypes early in development.
Implementation Considerations
- Requires expertise in microfluidics, yeast genetics, and single-cell RNA sequencing library preparation.
- Depends on access to droplet generation devices, fluorinated oils, surfactants, and downstream sequencing and bioinformatics infrastructure.
- Necessitates cross-team standardization of hydrogel preparation, spheroplasting, and bead coupling protocols for consistent results.
- Involves optimization of incubation time and media to prevent hydrogel overgrowth and cell escape, which is strain-dependent.
- Limited by the need for transcriptional biomarkers with known phenotype associations to enable accurate phenotypic inference from RNA-seq data.
Biopharma FAQs
Why does transcriptome-wide profiling matter for target validation in yeast strains?
Transcriptome-wide profiling enables detection of both expected and unexpected functional consequences of genetic modifications, providing a systems-level view that supports confident target validation by revealing on- and off-target effects across pathways.
How does isolating individual yeast colonies in droplets improve discovery pipeline accuracy?
Isolating isogenic colonies in droplets ensures clonal purity and prevents cross-contamination, enabling accurate attribution of gene expression profiles to specific genetic backgrounds, which is essential for reliable target-to-phenotype mapping in strain engineering.
What quantitative gene expression measurements enable strain comparison and selection?
Normalized transcript counts from RNA sequencing allow quantitative comparison of gene expression across strains, facilitating identification of expression signatures linked to desired phenotypes such as virulence switching or metabolic productivity.
Why are replication requirements important for cross-functional collaboration in strain screening?
Replication ensures that observed expression patterns are robust and not due to technical noise or clonal variation, which is critical for aligning discovery, screening, and preclinical teams around consistent strain performance data.
What statistical analysis capabilities are required before implementing this method in a discovery workflow?
Capabilities in dimensionality reduction (e.g., PCA, tSNE), clustering, and differential expression analysis are needed to interpret transcriptome data, identify phenotypic clusters, and link gene expression changes to specific genetic modifications or environmental conditions.
