Single cell dissociation of intestinal gut tissue for scRNAseq of all cell types v1 (protocols.io.bq94mz8w)

AbstractSingle-cell sequencing technologies are revolutionizing biology, but are limited by the need to dissociate fresh samples that can only be fixed at later stages. We present ACME (ACetic-MEthanol) dissociation, a cell dissociation approach that fixes cells as they are being dissociated. ACME-dissociated cells have high RNA integrity, can be cryopreserved multiple times, can be sorted by Fluorescence-Activated Cell Sorting (FACS) and are permeable, enabling combinatorial single-cell transcriptomic approaches. As a proof of principle, we have performed SPLiT-seq with ACME cells to obtain around ∼34K single cell transcriptomes from two planarian species and identified all previously described cell types in similar proportions. ACME is based on affordable reagents, can be done in most laboratories and even in the field, and thus will accelerate our knowledge of cell types across the tree of life.

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Paediatric gastric organoids as a tool for disease modelling and clinical translation

Pediatric Surgery International ◽

10.1007/s00383-020-04821-x ◽

2021 ◽

Vol 37 (3) ◽

pp. 317-324

Author(s):

Brendan C. Jones ◽

Giuseppe Calà ◽

Paolo De Coppi ◽

Giovanni Giuseppe Giobbe

Keyword(s):

Epithelial Cell ◽

Single Cell ◽

3D Culture ◽

Cell Types ◽

Defined Medium ◽

Gastric Epithelium ◽

Apical Surface ◽

Gastric Glands ◽

Cell Dissociation

Abstract Purpose Knowledge of gastric epithelial homeostasis remains incomplete, lacking human-specific models for study. This study establishes a protocol for deriving gastric epithelial organoids from paediatric gastric biopsies, providing a platform for modelling disease and developing translational therapies. Methods Full-thickness surgical samples and endoscopic mucosal biopsies were obtained from six patients. Gastric glands were isolated by a chemical chelation protocol and then plated in 3D culture in Matrigel® droplets in chemically defined medium. After formation, organoids were passaged by single cell dissociation or manual disaggregation. Cell composition and epithelial polarity of organoids were assessed by bright field microscopy and immunofluorescence analysis, comparing them to native paediatric gastric tissue. Results Gastric glands were successfully isolated from all six patients who were aged 4 months to 16 years. Gastric glands from all patients sealed to form spherical gastric organoids. These organoids could be passaged by manual disaggregation or single cell dissociation, remaining proliferative up to 1 year in culture. Organoids retained normal epithelial cell polarity, with the apical surface orientated towards the central lumen. Organoids expressed markers of mature gastric epithelial cell types, except for parietal cells. Conclusion Gastric organoids can be reliably generated from paediatric biopsies and are a representative in vitro model for studying gastric epithelium.

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Faculty Opinions recommendation of Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.732909626.793544340 ◽

2018 ◽

Author(s):

Bruce Appel

Keyword(s):

Single Cell ◽

Cell Types ◽

Vertebrate Brain ◽

Single Cell Profiling

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Prioritization of cell types responsive to biological perturbations in single-cell data with Augur

Nature Protocols ◽

10.1038/s41596-021-00561-x ◽

2021 ◽

Author(s):

Jordan W. Squair ◽

Michael A. Skinnider ◽

Matthieu Gautier ◽

Leonard J. Foster ◽

Grégoire Courtine

Keyword(s):

Single Cell ◽

Cell Types ◽

Cell Data

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Identifying cell types from single-cell data based on similarities and dissimilarities between cells

BMC Bioinformatics ◽

10.1186/s12859-020-03873-z ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Yuanyuan Li ◽

Ping Luo ◽

Yi Lu ◽

Fang-Xiang Wu

Keyword(s):

Gene Expression ◽

Single Cell ◽

Spectral Clustering ◽

Incidence Matrix ◽

Expression Patterns ◽

Cell Types ◽

Clustering Method ◽

Different Types ◽

Cell Data ◽

Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

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Adult tissue-resident stem cells—fact or fiction?

Stem Cell Research & Therapy ◽

10.1186/s13287-021-02142-x ◽

2021 ◽

Vol 12 (1) ◽

Cited By ~ 1

Author(s):

Deepa Bhartiya

Keyword(s):

Stem Cells ◽

Single Cell ◽

Adult Stem Cells ◽

Cell Types ◽

Specific Cell ◽

Tissue Specific ◽

Cardiac Tissues ◽

Adult Tissues ◽

Resident Stem Cells ◽

Abundant Cytoplasm

AbstractLife-long tissue homeostasis of adult tissues is supposedly maintained by the resident stem cells. These stem cells are quiescent in nature and rarely divide to self-renew and give rise to tissue-specific “progenitors” (lineage-restricted and tissue-committed) which divide rapidly and differentiate into tissue-specific cell types. However, it has proved difficult to isolate these quiescent stem cells as a physical entity. Recent single-cell RNAseq studies on several adult tissues including ovary, prostate, and cardiac tissues have not been able to detect stem cells. Thus, it has been postulated that adult cells dedifferentiate to stem-like state to ensure regeneration and can be defined as cells capable to replace lost cells through mitosis. This idea challenges basic paradigm of development biology regarding plasticity that a cell enters point of no return once it initiates differentiation. The underlying reason for this dilemma is that we are putting stem cells and somatic cells together while processing for various studies. Stem cells and adult mature cell types are distinct entities; stem cells are quiescent, small in size, and with minimal organelles whereas the mature cells are metabolically active and have multiple organelles lying in abundant cytoplasm. As a result, they do not pellet down together when centrifuged at 100–350g. At this speed, mature cells get collected but stem cells remain buoyant and can be pelleted by centrifuging at 1000g. Thus, inability to detect stem cells in recently published single-cell RNAseq studies is because the stem cells were unknowingly discarded while processing and were never subjected to RNAseq. This needs to be kept in mind before proposing to redefine adult stem cells.

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Optimizing expression quantitative trait locus mapping workflows for single-cell studies

Genome Biology ◽

10.1186/s13059-021-02407-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Anna S. E. Cuomo ◽

Giordano Alvari ◽

Christina B. Azodi ◽

Davis J. McCarthy ◽

Marc Jan Bonder ◽

...

Keyword(s):

Gene Expression ◽

Quantitative Trait Locus ◽

Single Cell ◽

Quantitative Trait ◽

Cell Types ◽

Expression Quantitative Trait Locus ◽

Eqtl Mapping ◽

Trait Locus ◽

The Impact

Abstract Background Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. Results While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. Conclusion We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

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Single-cell transcriptomic analysis of mIHC images via antigen mapping

Science Advances ◽

10.1126/sciadv.abc5464 ◽

2021 ◽

Vol 7 (10) ◽

pp. eabc5464

Author(s):

Kiya W. Govek ◽

Emma C. Troisi ◽

Zhen Miao ◽

Rachael G. Aubin ◽

Steven Woodhouse ◽

...

Keyword(s):

Single Cell ◽

Spatial Patterns ◽

Cell Types ◽

Level Of Detail ◽

Cell Populations ◽

Sequencing Data ◽

Spatially Resolved ◽

Murine Spleen ◽

Single Cell Rna Sequencing ◽

Antibody Panel

Highly multiplexed immunohistochemistry (mIHC) enables the staining and quantification of dozens of antigens in a tissue section with single-cell resolution. However, annotating cell populations that differ little in the profiled antigens or for which the antibody panel does not include specific markers is challenging. To overcome this obstacle, we have developed an approach for enriching mIHC images with single-cell RNA sequencing data, building upon recent experimental procedures for augmenting single-cell transcriptomes with concurrent antigen measurements. Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA) performs transcriptome-guided annotation of highly multiplexed cytometry datasets. It increases the level of detail in histological analyses by enabling the systematic annotation of nuanced cell populations, spatial patterns of transcription, and interactions between cell types. We demonstrate the utility of STvEA by uncovering the architecture of poorly characterized cell types in the murine spleen using published cytometry and mIHC data of this organ.

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treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses

Genome Biology ◽

10.1186/s13059-021-02368-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ruizhu Huang ◽

Charlotte Soneson ◽

Pierre-Luc Germain ◽

Thomas S.B. Schmidt ◽

Christian Von Mering ◽

...

Keyword(s):

Single Cell ◽

Synthetic Data ◽

Cell Types ◽

Data Driven ◽

Rna Seq ◽

Hierarchical Trees

AbstracttreeclimbR is for analyzing hierarchical trees of entities, such as phylogenies or cell types, at different resolutions. It proposes multiple candidates that capture the latent signal and pinpoints branches or leaves that contain features of interest, in a data-driven way. It outperforms currently available methods on synthetic data, and we highlight the approach on various applications, including microbiome and microRNA surveys as well as single-cell cytometry and RNA-seq datasets. With the emergence of various multi-resolution genomic datasets, treeclimbR provides a thorough inspection on entities across resolutions and gives additional flexibility to uncover biological associations.

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Single-cell transcriptomics following ischemic injury identifies a role for B2M in cardiac repair

Communications Biology ◽

10.1038/s42003-020-01636-3 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Bas Molenaar ◽

Louk T. Timmer ◽

Marjolein Droog ◽

Ilaria Perini ◽

Danielle Versteeg ◽

...

Keyword(s):

Single Cell ◽

Communication Networks ◽

Cardiac Remodeling ◽

Cardiac Injury ◽

Ischemic Injury ◽

Cell Types ◽

Repair Process ◽

Cardiac Repair ◽

Cellular Heterogeneity ◽

Intercellular Signaling

AbstractThe efficiency of the repair process following ischemic cardiac injury is a crucial determinant for the progression into heart failure and is controlled by both intra- and intercellular signaling within the heart. An enhanced understanding of this complex interplay will enable better exploitation of these mechanisms for therapeutic use. We used single-cell transcriptomics to collect gene expression data of all main cardiac cell types at different time-points after ischemic injury. These data unveiled cellular and transcriptional heterogeneity and changes in cellular function during cardiac remodeling. Furthermore, we established potential intercellular communication networks after ischemic injury. Follow up experiments confirmed that cardiomyocytes express and secrete elevated levels of beta-2 microglobulin in response to ischemic damage, which can activate fibroblasts in a paracrine manner. Collectively, our data indicate phase-specific changes in cellular heterogeneity during different stages of cardiac remodeling and allow for the identification of therapeutic targets relevant for cardiac repair.

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