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Sampling time-dependent artifacts in single-cell genomics studies.

Ramon Massoni-Badosa¹, Giovanni Iacono¹, Catia Moutinho¹

¹CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.

|May 13, 2020

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This summary is machine-generated.

Technical biases in single-cell RNA and ATAC sequencing can affect results. This study identifies sampling-related artifacts and offers solutions to ensure reproducible data from large-scale experiments.

Area of Science:

Genomics and Molecular Biology
Bioinformatics
Clinical Research

Background:

Large-scale single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) are powerful tools for biological and clinical studies.
Technical biases during sample acquisition can compromise the reliability and reproducibility of these high-throughput experiments.
Systematic benchmarking is crucial to identify and mitigate these biases before large-scale data generation.

Purpose of the Study:

To identify and quantify gene expression and chromatin accessibility artifacts introduced during the sampling phase of single-cell experiments.
To develop and validate experimental and computational strategies for preventing these sampling-induced biases.
To ensure the robustness and reproducibility of scRNA-seq and scATAC-seq data in biobank and clinical cohort studies.

Keywords:

Benchmarking Biobank CLL Chronic lymphocytic leukemia Cryopreservation PBMC Peripheral blood mononuclear cells RNA sequencing Sampling Single-cell

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Main Methods:

Analysis of gene expression and chromatin accessibility data from samples subjected to varying acquisition protocols.
Development of computational pipelines to detect and correct for sampling-related biases.
Experimental validation of proposed solutions using simulated and real-world biological samples.

Main Results:

Demonstration of significant gene expression and chromatin accessibility artifacts stemming from sample acquisition procedures.
Quantification of the extent of these artifacts across different experimental conditions.
Identification of specific experimental manipulations and computational algorithms effective in mitigating these biases.

Conclusions:

Sample acquisition introduces technical biases that can significantly impact single-cell RNA and ATAC sequencing data.
Implementing the identified experimental and computational solutions is essential for generating robust and reproducible results.
This work provides a framework for quality control in large-scale single-cell genomics studies on clinical and biobank samples.