A comprehensive mouse kidney atlas enables rare cell population characterization and robust marker discovery

Novella-Rausell, Claudio; Grudniewska, Magda; Peters, Dorien J.M.; Mahfouz, Ahmed

doi:10.1016/j.isci.2023.106877

Cited by 11 publications

(4 citation statements)

References 65 publications

(123 reference statements)

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“…Here, we completely reanalyzed four datasets using the raw data (from fastq) whose publications have had a significant impact on the single-cell community: a kidney dataset (10x Genomics and Smart-seq2), which has received (at the time of writing this manuscript) more than 1400 citations and is part of the Tabula Muris consortium 2 ; a liver dataset, which has received more than 780 citations and is part of the Human Cell Atlas 14 ; and a retina dataset, which has received more than 190 citations and is also part of the Human Cell Atlas 15 . Otherwise, the processed data was used to explore a mouse kidney cell atlas recently published by Novella-Rausell et al 16 , the Tabula Muris Senis dataset and the Tabula Sapiens dataset, with more than 140000, 356000 and 480000 cells respectively, confirming the value of MALAT1 expression in the absence of the intron fraction quality metric.…”

Section: Introductionmentioning

confidence: 80%

Revealing the Prevalence of Suboptimal Cells and Organs in Reference Cell Atlases: An Imperative for Enhanced Quality Control

Montserrat-Ayuso,

Esteve-Codina

2024

Preprint

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The advent of droplet-based single-cell RNA-sequencing (scRNA-seq) has dramatically increased data throughput, enabling the release of a diverse array of tissue cell atlases to the public. However, we will show that prominent initiatives such as the Human Cell Atlas, Tabula Muris, and Tabula Sapiens contain a significant amount of contamination products (frequently affecting the whole organ) in their data portals due to suboptimal quality filtering. Our work addresses a critical gap by advocating for more stringent quality filtering, highlighting the imperative for a shift from existing standards, which currently lean towards greater permissiveness. We will show the importance of incorporating cell intronic fraction in quality control -or MALAT1 expression otherwise- showcasing its informative nature and potential to elevate cell atlas data reliability. In summary, here, we unveil the hidden intronic landscape of every tissue and highlight the importance of more rigorous single-cell RNA-sequencing quality assessment in cell atlases to enhance their applicability in diverse downstream analyses.

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Section: Introductionmentioning

confidence: 80%

Revealing the Prevalence of Suboptimal Cells and Organs in Reference Cell Atlases: An Imperative for Enhanced Quality Control

Montserrat-Ayuso,

Esteve-Codina

2024

Preprint

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“…Visualization of cell clusters on a UMAP plot identified at least 12 distinct kidney cell populations (Figure 3D,E). We annotated these UMAP clusters based on their differential gene expression using the adult mouse kidney atlas (MKA) (Novella-Rausell et al, 2023). The largest class of cells consisted of proximal tubule (PT) cells which could be further classified into 4 distinct cell type clusters representing different segments of the tubule -segments 1, a mixture of segments 1 and 2, segment 3 and segment 3 type 2 (PTS1, PT(S1-S2), PTS3 and PTS3T2).…”

Section: Single-nucleus Profiling Of Adult Mouse Kidneymentioning

confidence: 99%

High-throughput gene expression analysis with TempO-LINC sensitively resolves complex brain, lung and kidney heterogeneity at single-cell resolution

Eastburn,

White,

Jayne

et al. 2024

Preprint

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We report the development and performance of a novel genomics platform, TempO-LINC, for conducting high-throughput transcriptomic analysis on single cells and nuclei. TempO-LINC works by adding cell-identifying molecular barcodes onto highly selective and high-sensitivity gene expression probes within fixed cells, without having to first generate cDNA. Using an instrument-free combinatorial-indexing approach, all probes within the same fixed cell receive an identical barcode, enabling the reconstruction of single-cell gene expression profiles across as few as several hundred cells and up to 100,000+ cells per run. The TempO-LINC approach is easily scalable based on the number of barcodes and rounds of barcoding performed; however, for the experiments reported in this study, the assay utilized over 5.3 million unique barcodes. TempO-LINC has a robust protocol for fixing and banking cells and displays high-sensitivity gene detection from multiple diverse sample types. We show that TempO-LINC has an observed multiplet rate of less than 1.1% and a cell capture rate of ∼50%. Although the assay can accurately profile the whole transcriptome (19,683 human or 21,400 mouse genes), it can be targeted to measure only actionable/informative genes and molecular pathways of interest – thereby reducing sequencing requirements. In this study, we applied TempO-LINC to profile the transcriptomes of 89,722 cells across multiple sample types, including nuclei from mouse lung, kidney and brain tissues. The data demonstrated the ability to identify and annotate at least 50 unique cell populations and positively correlate expression of cell type-specific molecular markers within them. TempO-LINC is a robust new single-cell technology that is ideal for large-scale applications/studies across thousands of samples with high data quality.

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“…To accelerate the construction of reference atlases, we developed scHPL: a method to automatically match the cell-type labels of multiple datasets and construct a cell-type hierarchy ( 10 ). In follow-up, Novella-Rausell et al showed how scHPL simplified the process when building a mouse kidney atlas ( 11 ).…”

Section: Introductionmentioning

confidence: 99%

Single-cell reference mapping to construct and extend cell-type hierarchies

Michielsen

Lotfollahi

Strobl

et al. 2023

NAR Genomics and Bioinformatics

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Single-cell genomics is now producing an ever-increasing amount of datasets that, when integrated, could provide large-scale reference atlases of tissue in health and disease. Such large-scale atlases increase the scale and generalizability of analyses and enable combining knowledge generated by individual studies. Specifically, individual studies often differ regarding cell annotation terminology and depth, with different groups specializing in different cell type compartments, often using distinct terminology. Understanding how these distinct sets of annotations are related and complement each other would mark a major step towards a consensus-based cell-type annotation reflecting the latest knowledge in the field. Whereas recent computational techniques, referred to as ‘reference mapping’ methods, facilitate the usage and expansion of existing reference atlases by mapping new datasets (i.e. queries) onto an atlas; a systematic approach towards harmonizing dataset-specific cell-type terminology and annotation depth is still lacking. Here, we present ‘treeArches’, a framework to automatically build and extend reference atlases while enriching them with an updatable hierarchy of cell-type annotations across different datasets. We demonstrate various use cases for treeArches, from automatically resolving relations between reference and query cell types to identifying unseen cell types absent in the reference, such as disease-associated cell states. We envision treeArches enabling data-driven construction of consensus atlas-level cell-type hierarchies and facilitating efficient usage of reference atlases.

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A comprehensive mouse kidney atlas enables rare cell population characterization and robust marker discovery

Cited by 11 publications

References 65 publications

Revealing the Prevalence of Suboptimal Cells and Organs in Reference Cell Atlases: An Imperative for Enhanced Quality Control

Revealing the Prevalence of Suboptimal Cells and Organs in Reference Cell Atlases: An Imperative for Enhanced Quality Control

High-throughput gene expression analysis with TempO-LINC sensitively resolves complex brain, lung and kidney heterogeneity at single-cell resolution

Single-cell reference mapping to construct and extend cell-type hierarchies

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