简介：
Overview
This protocol outlines a method for obtaining fluorescence microscopy movies of growing yeast cells and extracting single-cell time series data. The approach integrates automated lineage tracking with manual curation to ensure accurate data analysis.
Key Study Components
Area of Science
- Cell Biology
- Fluorescence Microscopy
- Yeast Cell Analysis
Background
- Fluorescence microscopy is a powerful tool for studying live cells.
- Yeast cells serve as a model organism for cellular processes.
- Tracking cell lineage is crucial for understanding growth dynamics.
- Automated software can enhance data accuracy and efficiency.
Purpose of Study
- To develop a protocol for capturing time-lapse fluorescence movies of yeast cells.
- To provide a software tool for analyzing single-cell data.
- To assess transcriptional changes during the cell cycle.
Methods Used
- Culturing yeast cells in a microfluidic chamber.
- Performing time-lapse fluorescence microscopy.
- Using graft software for cell tracking and measurement.
- Analyzing lineage assignments and exporting expression time series.
Main Results
- Successful acquisition of fluorescence movies of yeast cells.
- Automated tracking of cell lineage and division times.
- Extraction of transcription rate changes in response to factors.
- Integration of visual inspection for data validation.
Conclusions
- The protocol enables detailed analysis of yeast cell dynamics.
- Automated tools improve the efficiency of data collection.
- Insights into transcriptional regulation can be gained through this method.
What is the main goal of this protocol?
The main goal is to obtain fluorescence movies of growing yeast cells and extract time series data for analysis.
How are the yeast cells cultured?
Yeast cells are cultured in a microfluidic chamber to maintain a monolayer for imaging.
What software is used for data analysis?
The graft software package is used for tracking and measuring cells over time.
What type of microscopy is employed?
Time-lapse fluorescence microscopy is employed to capture cell dynamics.
How are transcription rates inferred?
Transcription rates are inferred from the extracted expression time series data.
What is the significance of lineage tracking?
Lineage tracking is significant for understanding cell division and growth patterns.
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