Researchers at the University of Michigan have developed a computational tool called Originator to improve the accuracy of single-cell RNA sequencing (scRNA-seq). One common challenge in scRNA-seq is contamination from blood-derived immune cells during tissue sample preparation, which can distort results and make it difficult to study tissue-resident cells properly.
The Originator tool helps researchers distinguish between immune cells originating from the bloodstream and those that naturally reside in tissues. It also identifies cells with different genetic backgrounds, which is crucial for studying complex tissues like tumors or the placenta. The study demonstrated Originator’s accuracy using a mix of artificial and real datasets, including pancreatic cancer and placental samples.
Illustration of Originator framework and the prevalence of blood immune cells in tissue samples
a The input data are the scRNA-seq experiment on tissue sections. (1) Data preprocessing and cell type annotation. (2) Separating barcoded cells into different origins by blood vs. tissue residents context and optionally by inferred genotype information. (3) Using the results in steps (1) and (2) to dissect tissue heterogeneity. (4) The functional downstream analyses with respect to cells’ origins. b Heatmap plots show inferred immune cell type proportions originated from the blood (left) vs. the tissue (right), from scRNA-seq data of a wide-variety of organs including liver, lung, spleen, kidney, and pancreas and in the normal or cancer tissues. The cell types were annotated by the original publications and blood vs. tissue identification was done by Originator. NA: the cell type does not exist in the original publication
By filtering out unwanted contamination, Originator enhances the reliability of RNA sequencing data, leading to better insights into disease mechanisms and tissue-specific biology. This tool has the potential to improve studies on cancer, immunology, and developmental biology, making scRNA-seq a more powerful technique for biomedical research.
Availability – originator is freely accessible on Github at https://github.com/lanagarmire/Originator
Unjitwattana T, Huang Q, Yang Y, Tao L, Yang Y, Zhou M, Du Y, Garmire LX. (2025) Single-cell RNA-seq data have prevalent blood contamination but can be rescued by Originator, a computational tool separating single-cell RNA-seq by genetic and contextual information. Genome Biol 26(1):52. [article]
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Researchers at the University of Michigan have developed a computational tool called Originator to improve the accuracy of single-cell RNA sequencing (scRNA-seq). One common challenge in scRNA-seq is contamination from blood-derived immune cells during tissue sample preparation, which can distort results and make it difficult to study tissue-resident cells properly.
The Originator tool helps researchers distinguish between immune cells originating from the bloodstream and those that naturally reside in tissues. It also identifies cells with different genetic backgrounds, which is crucial for studying complex tissues like tumors or the placenta. The study demonstrated Originator’s accuracy using a mix of artificial and real datasets, including pancreatic cancer and placental samples.
Illustration of Originator framework and the prevalence of blood immune cells in tissue samples
a The input data are the scRNA-seq experiment on tissue sections. (1) Data preprocessing and cell type annotation. (2) Separating barcoded cells into different origins by blood vs. tissue residents context and optionally by inferred genotype information. (3) Using the results in steps (1) and (2) to dissect tissue heterogeneity. (4) The functional downstream analyses with respect to cells’ origins. b Heatmap plots show inferred immune cell type proportions originated from the blood (left) vs. the tissue (right), from scRNA-seq data of a wide-variety of organs including liver, lung, spleen, kidney, and pancreas and in the normal or cancer tissues. The cell types were annotated by the original publications and blood vs. tissue identification was done by Originator. NA: the cell type does not exist in the original publication
By filtering out unwanted contamination, Originator enhances the reliability of RNA sequencing data, leading to better insights into disease mechanisms and tissue-specific biology. This tool has the potential to improve studies on cancer, immunology, and developmental biology, making scRNA-seq a more powerful technique for biomedical research.
Availability – originator is freely accessible on Github at https://github.com/lanagarmire/Originator
Unjitwattana T, Huang Q, Yang Y, Tao L, Yang Y, Zhou M, Du Y, Garmire LX. (2025) Single-cell RNA-seq data have prevalent blood contamination but can be rescued by Originator, a computational tool separating single-cell RNA-seq by genetic and contextual information. Genome Biol 26(1):52. [article]
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