Single-Cell Atlas Unveils Cellular Heterogeneity and Novel Markers in Human Neonatal and Adult Intervertebral Discs

AbstractA comprehensive understanding of the cellular heterogeneity and molecular mechanisms underlying the development, homeostasis, and disease of human intervertebral disks (IVDs) remains challenging. Here, the transcriptomic landscape of 108 108 IVD cells was mapped using single-cell RNA sequencing of three main compartments from young and adult healthy IVDs, including the nucleus pulposus (NP), annulus fibrosus, and cartilage endplate (CEP). The chondrocyte subclusters were classified based on their potential regulatory, homeostatic, and effector functions in extracellular matrix (ECM) homeostasis. Notably, in the NP, a PROCR+ resident progenitor population showed enriched colony-forming unit-fibroblast (CFU-F) activity and trilineage differentiation capacity. Finally, intercellular crosstalk based on signaling network analysis uncovered that the PDGF and TGF-β cascades are important cues in the NP microenvironment. In conclusion, a single-cell transcriptomic atlas that resolves spatially regulated cellular heterogeneity together with the critical signaling that underlies homeostasis will help to establish new therapeutic strategies for IVD degeneration in the clinic.

Download Full-text

Single-cell Atlas Unveils Cellular Heterogeneity and Novel Markers in Human Neonatal and Adult Intervertebral Discs

10.1101/2021.10.20.465219 ◽

2021 ◽

Author(s):

Wensen Jiang ◽

Juliane Dagmar Glaeser ◽

Khosrowdad Salehi ◽

Giselle Kaneda ◽

Pranav Mathkar ◽

...

Keyword(s):

Single Cell ◽

Cell Biology ◽

Annulus Fibrosus ◽

Developmental Trajectories ◽

Intervertebral Discs ◽

Cellular Heterogeneity ◽

Gradual Transition ◽

Nucleus Pulposus Cells ◽

And Function ◽

Ivd Degeneration

The origin, composition, distribution, and function of cells in the human intervertebral disc (IVD) has not been fully understood. Here, cell atlases of both human neonatal and adult IVDs have been generated and further assessed by gene ontology pathway enrichment, pseudo-time trajectory, histology, and immunofluorescence. Comparison of cell atlases revealed the presence of several sub-populations of notochordal cells (NC) in the neonatal IVD and a small quantity of NCs and associated markers in the adult IVD. Developmental trajectories predicted that most neonatal NCs develop into adult nucleus pulposus cells (NPCs) while some keep their identity throughout adulthood. A high heterogeneity and gradual transition of annulus fibrosus cells (AFCs) in the neonatal IVD was detected and their potential relevance in IVD development was assessed. Collectively, comparing single-cell atlases between neonatal and adult IVDs delineates the landscape of IVD cell biology and may help discover novel therapeutic targets for IVD degeneration.

Download Full-text

Single-Cell Rna-Seq Reveals Cellular Heterogeneity of Pluripotency Transition and X-Chromosome Dynamics During Early Postimplantation Mouse Development

SSRN Electronic Journal ◽

10.2139/ssrn.3231846 ◽

2018 ◽

Author(s):

Shangli Cheng ◽

Yu Pei ◽

Liqun He ◽

Guangdun Peng ◽

Björn Reinius ◽

...

Keyword(s):

Single Cell ◽

X Chromosome ◽

Cellular Heterogeneity ◽

Rna Seq ◽

Mouse Development ◽

Chromosome Dynamics

Download Full-text

Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

Genome Medicine ◽

10.1186/s13073-021-00885-z ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Sunny Z. Wu ◽

Daniel L. Roden ◽

Ghamdan Al-Eryani ◽

Nenad Bartonicek ◽

Kate Harvey ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Single Cells ◽

Cellular Heterogeneity ◽

Tumour Heterogeneity ◽

Fresh Tissue ◽

Human Cancers ◽

Cryopreserved Cell ◽

Single Cell Rna Sequencing

Abstract Background High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms. Methods Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments. Results Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. Conclusions We show that the viable cryopreservation of human cancers provides high-quality single-cells for multi-omics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies.

Download Full-text

Melanoma Single-Cell Biology in Experimental and Clinical Settings

Journal of Clinical Medicine ◽

10.3390/jcm10030506 ◽

2021 ◽

Vol 10 (3) ◽

pp. 506

Author(s):

Hans Binder ◽

Maria Schmidt ◽

Henry Loeffler-Wirth ◽

Lena Suenke Mortensen ◽

Manfred Kunz

Keyword(s):

T Cell ◽

Single Cell ◽

Intracellular Signaling ◽

Cell Biology ◽

Immune Cell ◽

Malignant Tumors ◽

Checkpoint Inhibitor ◽

Treatment Resistance ◽

Resistance Mechanisms ◽

Cellular Heterogeneity

Cellular heterogeneity is regarded as a major factor for treatment response and resistance in a variety of malignant tumors, including malignant melanoma. More recent developments of single-cell sequencing technology provided deeper insights into this phenomenon. Single-cell data were used to identify prognostic subtypes of melanoma tumors, with a special emphasis on immune cells and fibroblasts in the tumor microenvironment. Moreover, treatment resistance to checkpoint inhibitor therapy has been shown to be associated with a set of differentially expressed immune cell signatures unraveling new targetable intracellular signaling pathways. Characterization of T cell states under checkpoint inhibitor treatment showed that exhausted CD8+ T cell types in melanoma lesions still have a high proliferative index. Other studies identified treatment resistance mechanisms to targeted treatment against the mutated BRAF serine/threonine protein kinase including repression of the melanoma differentiation gene microphthalmia-associated transcription factor (MITF) and induction of AXL receptor tyrosine kinase. Interestingly, treatment resistance mechanisms not only included selection processes of pre-existing subclones but also transition between different states of gene expression. Taken together, single-cell technology has provided deeper insights into melanoma biology and has put forward our understanding of the role of tumor heterogeneity and transcriptional plasticity, which may impact on innovative clinical trial designs and experimental approaches.

Download Full-text

MBRS-46. CHARTING NEOPLASTIC AND IMMUNE CELL HETEROGENEITY IN HUMAN AND GEM MODELS OF MEDULLOBLASTOMA USING scRNAseq

Neuro-Oncology ◽

10.1093/neuonc/noaa222.555 ◽

2020 ◽

Vol 22 (Supplement_3) ◽

pp. iii406-iii406

Author(s):

Andrew Donson ◽

Kent Riemondy ◽

Sujatha Venkataraman ◽

Ahmed Gilani ◽

Bridget Sanford ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Genetically Engineered ◽

Cellular Heterogeneity ◽

Cell Heterogeneity ◽

Transcriptomic Data ◽

Single Cell Rna Sequencing ◽

Transcript Profiles

Abstract We explored cellular heterogeneity in medulloblastoma using single-cell RNA sequencing (scRNAseq), immunohistochemistry and deconvolution of bulk transcriptomic data. Over 45,000 cells from 31 patients from all main subgroups of medulloblastoma (2 WNT, 10 SHH, 9 GP3, 11 GP4 and 1 GP3/4) were clustered using Harmony alignment to identify conserved subpopulations. Each subgroup contained subpopulations exhibiting mitotic, undifferentiated and neuronal differentiated transcript profiles, corroborating other recent medulloblastoma scRNAseq studies. The magnitude of our present study builds on the findings of existing studies, providing further characterization of conserved neoplastic subpopulations, including identification of a photoreceptor-differentiated subpopulation that was predominantly, but not exclusively, found in GP3 medulloblastoma. Deconvolution of MAGIC transcriptomic cohort data showed that neoplastic subpopulations are associated with major and minor subgroup subdivisions, for example, photoreceptor subpopulation cells are more abundant in GP3-alpha. In both GP3 and GP4, higher proportions of undifferentiated subpopulations is associated with shorter survival and conversely, differentiated subpopulation is associated with longer survival. This scRNAseq dataset also afforded unique insights into the immune landscape of medulloblastoma, and revealed an M2-polarized myeloid subpopulation that was restricted to SHH medulloblastoma. Additionally, we performed scRNAseq on 16,000 cells from genetically engineered mouse (GEM) models of GP3 and SHH medulloblastoma. These models showed a level of fidelity with corresponding human subgroup-specific neoplastic and immune subpopulations. Collectively, our findings advance our understanding of the neoplastic and immune landscape of the main medulloblastoma subgroups in both humans and GEM models.

Download Full-text

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.

Download Full-text

Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining

npj Systems Biology and Applications ◽

10.1038/s41540-020-00162-6 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Luca Alessandri ◽

Francesca Cordero ◽

Marco Beccuti ◽

Nicola Licheri ◽

Maddalena Arigoni ◽

...

Keyword(s):

Quality Control ◽

Single Cell ◽

Medical Personnel ◽

Cellular Heterogeneity ◽

Biological Information ◽

Cell Clusters ◽

The Neural Network ◽

Rnaseq Data ◽

Functional Features ◽

Cell Subpopulations

AbstractSingle-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Thus, it would be of great interest being able to disclose biological information belonging to cell subpopulations, which can be defined by clustering analysis of scRNAseq data. In this manuscript, we report a tool that we developed for the functional mining of single cell clusters based on Sparsely-Connected Autoencoder (SCA). This tool allows uncovering hidden features associated with scRNAseq data. We implemented two new metrics, QCC (Quality Control of Cluster) and QCM (Quality Control of Model), which allow quantifying the ability of SCA to reconstruct valuable cell clusters and to evaluate the quality of the neural network achievements, respectively. Our data indicate that SCA encoded space, derived by different experimentally validated data (TF targets, miRNA targets, Kinase targets, and cancer-related immune signatures), can be used to grasp single cell cluster-specific functional features. In our implementation, SCA efficacy comes from its ability to reconstruct only specific clusters, thus indicating only those clusters where the SCA encoding space is a key element for cells aggregation. SCA analysis is implemented as module in rCASC framework and it is supported by a GUI to simplify it usage for biologists and medical personnel.

Download Full-text

Comprehensive analysis of single cell ATAC-seq data with SnapATAC

Nature Communications ◽

10.1038/s41467-021-21583-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Rongxin Fang ◽

Sebastian Preissl ◽

Yang Li ◽

Xiaomeng Hou ◽

Jacinta Lucero ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Expression Patterns ◽

Regulatory Elements ◽

Cellular Heterogeneity ◽

Specific Gene ◽

Open Chromatin ◽

Cell Type ◽

Process Data ◽

Cell Type Specific

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

Download Full-text

EPEN-31. SINGLE-CELL RNAseq OF CHILDHOOD EPENDYMOMA REVEALS DISTINCT NEOPLASTIC CELL SUBPOPULATIONS THAT IMPACT ETIOLOGY, MOLECULAR CLASSIFICATION AND OUTCOME

Neuro-Oncology ◽

10.1093/neuonc/noaa222.167 ◽

2020 ◽

Vol 22 (Supplement_3) ◽

pp. iii314-iii314

Author(s):

Andrew Donson ◽

Austin Gillen ◽

Riemondy Kent ◽

Ahmed Gilani ◽

Sujatha Venkataraman ◽

...

Keyword(s):

Single Cell ◽

Histological Analysis ◽

Molecular Classification ◽

Neoplastic Cell ◽

Good Prognosis ◽

Cellular Heterogeneity ◽

Mesenchymal Phenotype ◽

Ependymal Differentiation ◽

Cell Subpopulations ◽

Cell Niche

Abstract Ependymoma (EPN) is a brain tumor commonly presenting in childhood that remains fatal in the majority of children. Intra-tumoral cellular heterogeneity in bulk-tumor samples significantly confounds our understanding of EPN biology, impeding development of effective therapy. We therefore used single-cell RNA sequencing to catalog cellular heterogeneity of 26 childhood EPN, predominantly from ST-RELA, PFA1 and PFA2 subgroups. ST-RELA and PFA subgroups clustered separately, with ST-RELA clustering largely according to individual sample-of-origin. PFA1 and PFA2 subgroup EPNs cells were intermixed and revealed 4 major subpopulations – 2 with characteristics of ependymal differentiation (transporter and ciliated phenotype subpopulations), an undifferentiated subpopulation and a mesenchymal phenotype. Pseudotime analysis showed the undifferentiated progenitor subpopulation either differentiating into ependymal differentiation subpopulations or transitioning into the mesenchymal subpopulation. Histological analysis revealed that undifferentiated and mesenchymal subpopulations cells colocalized to perinecrotic/perivascular zones, the putative ependymoma stem cell niche. Deconvolution of PFA bulk transcriptome data showed that undifferentiated and mesenchymal subpopulations were associated with a poor prognosis; whereas the ciliated ependymal cell-differentiated subpopulation was associated with a good prognosis. In conflict with current distinct classification paradigms, the ratio of mesenchymal and ciliated subpopulations determined bulk-tumor subgroups assignment to PFA1 and PFA2 respectively. This atlas of EPN cellular heterogeneity provides an important advance in our understanding of EPN biology, identifying high-risk associated subpopulations for therapeutic targeting.

Download Full-text