Single-Cell RNA Sequencing Procedures and Data Analysis

Wolfien, Markus; Dávid, Róbert; Galow, Anne-Marie

doi:10.36255/exonpublications.bioinformatics.2021.ch2

Cited by 12 publications

(8 citation statements)

References 75 publications

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“…However, for successful ureter tissue engineering, several elements need to be considered, including physiological characteristics and local environment of the tissue, the type of scaffold used, and, most importantly, a detailed understanding of the individual cell types in the adult human ureter (Simaioforidis et al, 2013). Advancements in single-cell RNA sequencing (scRNA-seq) technologies have revolutionized our understanding of cellular complexity in a myriad of different tissue types, both in normal and diseased states (Wolfien et al, 2021). Single-cell transcriptomics has been used to study the mouse and human kidney (Combes et al, 2019;Lake et al, 2019;Menon et al, 2018;Sheng et al, 2021;Wu et al, 2018); however, these analyses have focused on interrogating the cellular composition of the kidney, with a focus on the kidney interstitium and nephrons, without describing the urothelial compartment.…”

Section: Introductionmentioning

confidence: 99%

Single-cell and spatial mapping Identify cell types and signaling Networks in the human ureter

Fink

Sona²,

Tran³

et al. 2022

Developmental Cell

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Section: Introductionmentioning

confidence: 99%

Single-cell and spatial mapping Identify cell types and signaling Networks in the human ureter

Fink

Sona²,

Tran³

et al. 2022

Developmental Cell

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“…For example, low transcript or gene numbers may be characteristic of quiescent cell populations and high counts may arise from large cells. Accordingly, thresholds are usually user-defined for each experiment individually based on specific guidelines [ 10 , 15 ]. For low-count filtering, the transcripts per cell are visualized and a threshold is applied, where count depths start to decrease rapidly.…”

Section: Discussionmentioning

confidence: 99%

Quality control in scRNA-Seq can discriminate pacemaker cells: the mtRNA bias

Galow

Kussauer

Wolfien

et al. 2021

Cell. Mol. Life Sci.

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Single-cell RNA-sequencing (scRNA-seq) provides high-resolution insights into complex tissues. Cardiac tissue, however, poses a major challenge due to the delicate isolation process and the large size of mature cardiomyocytes. Regardless of the experimental technique, captured cells are often impaired and some capture sites may contain multiple or no cells at all. All this refers to “low quality” potentially leading to data misinterpretation. Common standard quality control parameters involve the number of detected genes, transcripts per cell, and the fraction of transcripts from mitochondrial genes. While cutoffs for transcripts and genes per cell are usually user-defined for each experiment or individually calculated, a fixed threshold of 5% mitochondrial transcripts is standard and often set as default in scRNA-seq software. However, this parameter is highly dependent on the tissue type. In the heart, mitochondrial transcripts comprise almost 30% of total mRNA due to high energy demands. Here, we demonstrate that a 5%-threshold not only causes an unacceptable exclusion of cardiomyocytes but also introduces a bias that particularly discriminates pacemaker cells. This effect is apparent for our in vitro generated induced-sinoatrial-bodies (iSABs; highly enriched physiologically functional pacemaker cells), and also evident in a public data set of cells isolated from embryonal murine sinoatrial node tissue (Goodyer William et al. in Circ Res 125:379–397, 2019). Taken together, we recommend omitting this filtering parameter for scRNA-seq in cardiovascular applications whenever possible.

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“…Typical data processing of scRNA-Seq involves alignment, quality control, normalization, confounding factor identification, dimensionality reduction, and cell-gene level analysis [24]. Alignment of the raw data was conducted by using kallisto (v.0.46.1) for use case 1 and the CellRanger Software (v.6.1.1) provided by 10x Genomics for use case 2.…”

Section: Single-cell Data Analysismentioning

confidence: 99%

Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling

et al. 2021

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Background The research landscape of single-cell and single-nuclei RNA-sequencing is evolving rapidly. In particular, the area for the detection of rare cells was highly facilitated by this technology. However, an automated, unbiased, and accurate annotation of rare subpopulations is challenging. Once rare cells are identified in one dataset, it is usually necessary to generate further specific datasets to enrich the analysis (e.g., with samples from other tissues). From a machine learning perspective, the challenge arises from the fact that rare-cell subpopulations constitute an imbalanced classification problem. We here introduce a Machine Learning (ML)-based oversampling method that uses gene expression counts of already identified rare cells as an input to generate synthetic cells to then identify similar (rare) cells in other publicly available experiments. We utilize single-cell synthetic oversampling (sc-SynO), which is based on the Localized Random Affine Shadowsampling (LoRAS) algorithm. The algorithm corrects for the overall imbalance ratio of the minority and majority class. Results We demonstrate the effectiveness of our method for three independent use cases, each consisting of already published datasets. The first use case identifies cardiac glial cells in snRNA-Seq data (17 nuclei out of 8635). This use case was designed to take a larger imbalance ratio (~1 to 500) into account and only uses single-nuclei data. The second use case was designed to jointly use snRNA-Seq data and scRNA-Seq on a lower imbalance ratio (~1 to 26) for the training step to likewise investigate the potential of the algorithm to consider both single-cell capture procedures and the impact of “less” rare-cell types. The third dataset refers to the murine data of the Allen Brain Atlas, including more than 1 million cells. For validation purposes only, all datasets have also been analyzed traditionally using common data analysis approaches, such as the Seurat workflow. Conclusions In comparison to baseline testing without oversampling, our approach identifies rare-cells with a robust precision-recall balance, including a high accuracy and low false positive detection rate. A practical benefit of our algorithm is that it can be readily implemented in other and existing workflows. The code basis in R and Python is publicly available at FairdomHub, as well as GitHub, and can easily be transferred to identify other rare-cell types.

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Single-Cell RNA Sequencing Procedures and Data Analysis

Cited by 12 publications

References 75 publications

Single-cell and spatial mapping Identify cell types and signaling Networks in the human ureter

Single-cell and spatial mapping Identify cell types and signaling Networks in the human ureter

Quality control in scRNA-Seq can discriminate pacemaker cells: the mtRNA bias

Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling

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