A structured evaluation of cryopreservation in generating single cell transcriptomes from cerebrospinal fluid

Importance: A robust cerebrospinal fluid (CSF) cell cryopreservation protocol using high resolution single-cell (sc) transcriptomic data would enable the deployment of this important modality in multi-center translational research studies and clinical trials in which many sites do not have the expertise or resources to produce data from fresh samples. It would also serve to reduce technical variability in larger projects. Objective: To test a reliable cryopreservation protocol adapted for CSF cells, facilitating the characterization of these rare, fragile cells in moderate to large scale studies. Design: Diagnostic lumbar punctures were performed on twenty-one patients at two independent sites. Excess CSF was collected and cells were isolated. Each cell sample was split into two fractions for single cell analysis using one of two possible chemistries: 3′ sc-RNA-Sequencing or 5 ′ sc-RNA-Sequencing. One cell fraction was processed fresh while the second sample was cryopreserved and profiled at a later time after thawing. Setting: The research protocol was deployed at two academic medical centers taking care of multiple sclerosis and other neurological conditions. Participants: 21 subjects (age 24, 72) were recruited from individuals undergoing a diagnostic lumbar puncture for suspected neuroinflammatory disease or another neurologic illness; they donated excess CSF. Findings: Our comparison of fresh and cryopreserved data from the same individuals demonstrates highly efficient recovery of all known CSF cell types. The proportion of all cell types was similar between the fresh and the cryopreserved cells processed, and RNA expression was not significantly different. Results were comparable at both performance sites, and with different single cell sequencing chemistries. Cryopreservation also did not affect recovery of T and B cell clonotype diversity. Conclusion and relevance: Our cryopreservation protocol for CSF cells provides an important alternative to fresh processing of fragile CSF cells: cryopreservation enables the involvement of sites with limited capacity for experimental manipulation and reduces technical variation by enabling batch processing and pooling of samples.

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deepMNN: Deep Learning-Based Single-Cell RNA Sequencing Data Batch Correction Using Mutual Nearest Neighbors

Frontiers in Genetics ◽

10.3389/fgene.2021.708981 ◽

2021 ◽

Vol 12 ◽

Author(s):

Bin Zou ◽

Tongda Zhang ◽

Ruilong Zhou ◽

Xiaosen Jiang ◽

Huanming Yang ◽

...

Keyword(s):

Deep Learning ◽

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Cell Types ◽

Batch Effect ◽

Batch Effects ◽

Batch Correction ◽

Single Cell Rna Sequencing ◽

Identical Cell

It is well recognized that batch effect in single-cell RNA sequencing (scRNA-seq) data remains a big challenge when integrating different datasets. Here, we proposed deepMNN, a novel deep learning-based method to correct batch effect in scRNA-seq data. We first searched mutual nearest neighbor (MNN) pairs across different batches in a principal component analysis (PCA) subspace. Subsequently, a batch correction network was constructed by stacking two residual blocks and further applied for the removal of batch effects. The loss function of deepMNN was defined as the sum of a batch loss and a weighted regularization loss. The batch loss was used to compute the distance between cells in MNN pairs in the PCA subspace, while the regularization loss was to make the output of the network similar to the input. The experiment results showed that deepMNN can successfully remove batch effects across datasets with identical cell types, datasets with non-identical cell types, datasets with multiple batches, and large-scale datasets as well. We compared the performance of deepMNN with state-of-the-art batch correction methods, including the widely used methods of Harmony, Scanorama, and Seurat V4 as well as the recently developed deep learning-based methods of MMD-ResNet and scGen. The results demonstrated that deepMNN achieved a better or comparable performance in terms of both qualitative analysis using uniform manifold approximation and projection (UMAP) plots and quantitative metrics such as batch and cell entropies, ARI F1 score, and ASW F1 score under various scenarios. Additionally, deepMNN allowed for integrating scRNA-seq datasets with multiple batches in one step. Furthermore, deepMNN ran much faster than the other methods for large-scale datasets. These characteristics of deepMNN made it have the potential to be a new choice for large-scale single-cell gene expression data analysis.

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The single-cell eQTLGen consortium

eLife ◽

10.7554/elife.52155 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 18

Author(s):

MGP van der Wijst ◽

DH de Vries ◽

HE Groot ◽

G Trynka ◽

CC Hon ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Cell Types ◽

Eqtl Analysis ◽

Sequencing Data ◽

Scale Population ◽

Trait Locus ◽

Different Cell Types ◽

Affect Gene Expression

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.

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Single-cell RNA sequencing of adultDrosophilaovary identifies transcriptional programs governing oogenesis

10.1101/802314 ◽

2019 ◽

Author(s):

Allison Jevitt ◽

Deeptiman Chatterjee ◽

Gengqiang Xie ◽

Xian-Feng Wang ◽

Taylor Otwell ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Developmental Process ◽

Expression Profiles ◽

Follicle Cell ◽

Cell Types ◽

Broad Perspective ◽

Single Cell Rna Sequencing ◽

Egg Shell

AbstractOogenesis is a complex developmental process that involves spatiotemporally regulated coordination between the germline and supporting, somatic cell populations. This process has been modelled extensively using theDrosophilaovary. While different ovarian cell types have been identified through traditional means, the large-scale expression profiles underlying each cell type remain unknown. Using single-cell RNA sequencing technology, we have built a transcriptomic dataset for the adultDrosophilaovary and connected tissues. This dataset captures the entire transcriptional trajectory of the developing follicle cell population over time. Our findings provide detailed insight into processes such as cell-cycle switching, migration, symmetry breaking, nurse cell engulfment, egg-shell formation, and signaling during corpus luteum formation, marking a newly identified oogenesis-to-ovulation transition. Altogether, these findings provide a broad perspective on oogenesis at a single-cell resolution while revealing new genetic markers and fate-specific transcriptional signatures to facilitate future studies.

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Comparison of computational methods for imputing single-cell RNA-sequencing data

10.1101/241190 ◽

2017 ◽

Cited By ~ 10

Author(s):

Lihua Zhang ◽

Shihua Zhang

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Real Data ◽

Cell Types ◽

Biological Functions ◽

Sequencing Data ◽

Imputation Methods ◽

Future Studies ◽

Single Cell Rna Sequencing

AbstractSingle-cell RNA-sequencing (scRNA-seq) is a recent breakthrough technology, which paves the way for measuring RNA levels at single cell resolution to study precise biological functions. One of the main challenges when analyzing scRNA-seq data is the presence of zeros or dropout events, which may mislead downstream analyses. To compensate the dropout effect, several methods have been developed to impute gene expression since the first Bayesian-based method being proposed in 2016. However, these methods have shown very diverse characteristics in terms of model hypothesis and imputation performance. Thus, large-scale comparison and evaluation of these methods is urgently needed now. To this end, we compared eight imputation methods, evaluated their power in recovering original real data, and performed broad analyses to explore their effects on clustering cell types, detecting differentially expressed genes, and reconstructing lineage trajectories in the context of both simulated and real data. Simulated datasets and case studies highlight that there are no one method performs the best in all the situations. Some defects of these methods such as scalability, robustness and unavailability in some situations need to be addressed in future studies.

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CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA-sequencing

10.1101/374462 ◽

2018 ◽

Cited By ~ 1

Author(s):

Kenta Sato ◽

Koki Tsuyuzaki ◽

Kentaro Shimizu ◽

Itoshi Nikaido

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

State Of The Art ◽

Cell Types ◽

Cell Search ◽

Wide Range ◽

Single Cell Rna Sequencing ◽

Comparable Accuracy ◽

Multicellular Organisms

AbstractRecent technical improvements in single-cell RNA sequencing (scRNA-seq) have enabled massively parallel profiling of transcriptomes, thereby promoting large-scale studies encompassing a wide range of cell types of multicellular organisms. With this background, we propose CellFishing.jl, a new method for searching atlas-scale datasets for similar cells and detecting noteworthy genes of query cells with high accuracy and throughput. Using multiple scRNA-seq datasets, we validate that our method demonstrates comparable accuracy to and is markedly faster than the state-of-the-art software. Moreover, CellFishing.jl is scalable to more than one million cells, and the throughput of the search is approximately 1,600 cells per second.

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Single-cell transcriptome conservation in a comparative analysis of fresh and cryopreserved human skin tissue: pilot in localized scleroderma

Arthritis Research & Therapy ◽

10.1186/s13075-020-02343-4 ◽

2020 ◽

Vol 22 (1) ◽

Author(s):

Emily Mirizio ◽

Tracy Tabib ◽

Xiao Wang ◽

Wei Chen ◽

Christopher Liu ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Large Scale ◽

Single Cell Analysis ◽

Cell Types ◽

Skin Tissue ◽

Localized Scleroderma ◽

Specific Gene ◽

Tissue Samples ◽

Cryopreserved Skin

Abstract Background The purpose of this study was to assess variability in cell composition and cell-specific gene expression in the skin of patients with localized scleroderma (LS) utilizing CryoStor® CS10 in comparison to RPMI to produce adequate preservation of tissue samples and cell types of interest for use in large-scale multi-institutional collaborations studying localized scleroderma and other skin disorders. Methods We performed single-cell RNA sequencing on paired skin biopsy specimens from 3 patients with LS. Each patient with one sample cryopreserved in CryoStor® CS10 and one fresh in RPMI media using 10× Genomics sequencing. Results Levels of cell viability and yield were comparable between CryoStor® CS10 (frozen) and RPMI (fresh) preserved cells. Furthermore, gene expression between preservation methods was collectively significantly correlated and conserved across all 18 identified cell cluster populations. Conclusion Comparable cell population and transcript expression yields between CryoStor® CS10 and RPMI preserved cells support the utilization of cryopreserved skin tissue in single-cell analysis. This suggests that employing standardized cryopreservation protocols for the skin tissue will help facilitate multi-site collaborations looking to identify mechanisms of disease in disorders characterized by cutaneous pathology.

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Large-scale single-cell analysis reveals critical immune characteristics of COVID-19 patients

10.1101/2020.10.29.360479 ◽

2020 ◽

Cited By ~ 1

Author(s):

Xianwen Ren ◽

Wen Wen ◽

Xiaoying Fan ◽

Wenhong Hou ◽

Bin Su ◽

...

Keyword(s):

B Cell ◽

Single Cell ◽

Immune Cells ◽

Large Scale ◽

Immune Cell ◽

Single Cell Analysis ◽

Cell Types ◽

List Type ◽

Cell Responses ◽

B Cell Responses

SUMMARYDysfunctional immune response in the COVID-19 patients is a recurrent theme impacting symptoms and mortality, yet the detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 205 COVID-19 patients and controls to create a comprehensive immune landscape. Lymphopenia and active T and B cell responses were found to coexist and associated with age, sex and their interactions with COVID-19. Diverse epithelial and immune cell types were observed to be virus-positive and showed dramatic transcriptomic changes. Elevation of ANXA1 and S100A9 in virus-positive squamous epithelial cells may enable the initiation of neutrophil and macrophage responses via the ANXA1-FPR1 and S100A8/9-TLR4 axes. Systemic upregulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis and designing effective therapeutic strategies for COVID-19.HIGHLIGHTSLarge-scale scRNA-seq analysis depicts the immune landscape of COVID-19Lymphopenia and active T and B cell responses coexist and are shaped by age and sexSARS-CoV-2 infects diverse epithelial and immune cells, inducing distinct responsesCytokine storms with systemic S100A8/A9 are associated with COVID-19 severity

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Single-cell data clustering based on sparse optimization and low-rank matrix factorization

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab098 ◽

2021 ◽

Author(s):

Yinlei Hu ◽

Bin Li ◽

Falai Chen ◽

Kun Qu

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Matrix Factorization ◽

Data Clustering ◽

Cell Types ◽

Low Rank ◽

Sequencing Data ◽

Rank Matrix ◽

Single Cell Rna Sequencing ◽

Low Rank Matrix

Abstract Unsupervised clustering is a fundamental step of single-cell RNA sequencing data analysis. This issue has inspired several clustering methods to classify cells in single-cell RNA sequencing data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for single-cell RNA sequencing data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single-cell RNA sequencing data.

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Molecular characteristics and spatial distribution of adult human corneal cell subtypes

Scientific Reports ◽

10.1038/s41598-021-94933-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ann J. Ligocki ◽

Wen Fury ◽

Christian Gutierrez ◽

Christina Adler ◽

Tao Yang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cross Sections ◽

Cell Types ◽

Marker Genes ◽

Molecular Characteristics ◽

Transcriptional Level ◽

Human Cornea ◽

Adult Human ◽

And Migration

AbstractBulk RNA sequencing of a tissue captures the gene expression profile from all cell types combined. Single-cell RNA sequencing identifies discrete cell-signatures based on transcriptomic identities. Six adult human corneas were processed for single-cell RNAseq and 16 cell clusters were bioinformatically identified. Based on their transcriptomic signatures and RNAscope results using representative cluster marker genes on human cornea cross-sections, these clusters were confirmed to be stromal keratocytes, endothelium, several subtypes of corneal epithelium, conjunctival epithelium, and supportive cells in the limbal stem cell niche. The complexity of the epithelial cell layer was captured by eight distinct corneal clusters and three conjunctival clusters. These were further characterized by enriched biological pathways and molecular characteristics which revealed novel groupings related to development, function, and location within the epithelial layer. Moreover, epithelial subtypes were found to reflect their initial generation in the limbal region, differentiation, and migration through to mature epithelial cells. The single-cell map of the human cornea deepens the knowledge of the cellular subsets of the cornea on a whole genome transcriptional level. This information can be applied to better understand normal corneal biology, serve as a reference to understand corneal disease pathology, and provide potential insights into therapeutic approaches.

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