scholarly journals Study of cellular heterogeneity and differential dynamics of autophagy in human embryonic kidney development by single-cell RNA sequencing

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chen Wen-jin ◽  
Pan Xiu-wu ◽  
Chu Jian ◽  
Xu Da ◽  
Chen Jia-xin ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Intercellular Communication ◽  
Wilms Tumor ◽  
R Package ◽  
Cellular Heterogeneity ◽  
Transcriptional Level ◽  
Renal Cells ◽  
Cell Clusters ◽  
Single Cell Rna Sequencing

Abstract Background Autophagy is believed to participate in embryonic development, but whether the expression of autophagy-associated genes undergoes changes during the development of human embryonic kidneys remains unknown. Methods In this work, we identified 36,151 human renal cells from embryonic kidneys of 9–18 gestational weeks in 16 major clusters by single-cell RNA sequencing (scRNA-seq), and detected 1350 autophagy-related genes in all fetal renal cells. The abundance of each cell cluster in Wilms tumor samples from scRNA-seq and GDC TARGET WT datasets was detected by CIBERSORTx. R package Monocle 3 was used to determine differentiation trajectories. Cyclone tool of R package scran was applied to calculate the cell cycle scores. R package SCENIC was used to investigate the transcriptional regulons. The FindMarkers tool from Seurat was used to calculate DEGs. GSVA was used to perform gene set enrichment analyses. CellphoneDB was utilized to analyze intercellular communication. Results It was found that cells in the 13th gestational week showed the lowest transcriptional level in each cluster in all stages. Nephron progenitors could be divided into four subgroups with diverse levels of autophagy corresponding to different SIX2 expressions. SSBpod (podocyte precursors) could differentiate into four types of podocytes (Pod), and autophagy-related regulation was involved in this process. Pseudotime analysis showed that interstitial progenitor cells (IPCs) potentially possessed two primitive directions of differentiation to interstitial cells with different expressions of autophagy. It was found that NPCs, pretubular aggregates and interstitial cell clusters had high abundance in Wilms tumor as compared with para-tumor samples with active intercellular communication. Conclusions All these findings suggest that autophagy may be involved in the development and cellular heterogeneity of early human fetal kidneys. In addition, part of Wilms tumor cancer cells possess the characteristics of some fetal renal cell clusters. Graphical abstract

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

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.

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

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.

scholarly journals Single-cell RNA sequencing of cultured human endometrial CD140b+CD146+ perivascular cells highlights the importance of in vivo microenvironment

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dandan Cao ◽  
Rachel W. S. Chan ◽  
Ernest H. Y. Ng ◽  
Kristina Gemzell-Danielsson ◽  
William S. B. Yeung
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Marker Gene ◽  
Cellular Heterogeneity ◽  
Stromal Fibroblast ◽  
Perivascular Cells ◽  
Endometrial Cells ◽  
Single Cell Rna Sequencing

Abstract Background Endometrial mesenchymal-like stromal/stem cells (eMSCs) have been proposed as adult stem cells contributing to endometrial regeneration. One set of perivascular markers (CD140b&CD146) has been widely used to enrich eMSCs. Although eMSCs are easily accessible for regenerative medicine and have long been studied, their cellular heterogeneity, relationship to primary counterpart, remains largely unclear. Methods In this study, we applied 10X genomics single-cell RNA sequencing (scRNA-seq) to cultured human CD140b+CD146+ endometrial perivascular cells (ePCs) from menstrual and secretory endometrium. We also analyzed publicly available scRNA-seq data of primary endometrium and performed transcriptome comparison between cultured ePCs and primary ePCs at single-cell level. Results Transcriptomic expression-based clustering revealed limited heterogeneity within cultured menstrual and secretory ePCs. A main subpopulation and a small stress-induced subpopulation were identified in secretory and menstrual ePCs. Cell identity analysis demonstrated the similar cellular composition in secretory and menstrual ePCs. Marker gene expression analysis showed that the main subpopulations identified from cultured secretory and menstrual ePCs simultaneously expressed genes marking mesenchymal stem cell (MSC), perivascular cell, smooth muscle cell, and stromal fibroblast. GO enrichment analysis revealed that genes upregulated in the main subpopulation enriched in actin filament organization, cellular division, etc., while genes upregulated in the small subpopulation enriched in extracellular matrix disassembly, stress response, etc. By comparing subpopulations of cultured ePCs to the publicly available primary endometrial cells, it was found that the main subpopulation identified from cultured ePCs was culture-unique which was unlike primary ePCs or primary endometrial stromal fibroblast cells. Conclusion In summary, these data for the first time provides a single-cell atlas of the cultured human CD140b+CD146+ ePCs. The identification of culture-unique relatively homogenous cell population of CD140b+CD146+ ePCs underscores the importance of in vivo microenvironment in maintaining cellular identity.

scholarly journals Single-Cell RNA-Sequencing Identifies Infrapatellar Fat Pad Macrophage Polarization in Acute Synovitis/Fat Pad Fibrosis and Cell Therapy

2021 ◽  
Vol 8 (11) ◽  
pp. 166
Author(s):  
Dimitrios Kouroupis ◽  
Thomas M. Best ◽  
Lee D. Kaplan ◽  
Diego Correa ◽  
Anthony J. Griswold
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Transcriptional Profiling ◽  
Macrophage Polarization ◽  
Myeloid Cells ◽  
Cellular Heterogeneity ◽  
Joint Disease ◽  
Infrapatellar Fat Pad ◽  
Fat Pad ◽  
Single Cell Rna Sequencing

The pathogenesis and progression of knee inflammatory pathologies is modulated partly by residing macrophages in the infrapatellar fat pad (IFP), thus, macrophage polarization towards pro-inflammatory (M1) or anti-inflammatory (M2) phenotypes is important in joint disease pathologies. Alteration of M1/M2 balance contributes to the initiation and progression of joint inflammation and can be potentially altered with mesenchymal stem cell (MSC) therapy. In an acute synovial/IFP inflammation rat model a single intra-articular injection of IFP-MSC was performed, having as controls (1) diseased rats not receiving IFP-MSC and (2) non-diseased rats. After 4 days, cell specific transcriptional profiling via single-cell RNA-sequencing was performed on isolated IFP tissue from each group. Eight transcriptomically distinct cell populations were identified within the IFP across all three treatment groups with a noted difference in the proportion of myeloid cells across the groups. Largely myeloid cells consisted of macrophages (>90%); one M1 sub-cluster highly expressing pro-inflammatory markers and two M2 sub-clusters with one of them expressing higher levels of canonical M2 markers. Notably, the diseased samples (11.9%) had the lowest proportion of cells expressing M2 markers relative to healthy (14.8%) and MSC treated (19.4%) samples. These results suggest a phenotypic polarization of IFP macrophages towards the pro-inflammatory M1 phenotype in an acute model of inflammation, which are alleviated by IFP-MSC therapy inducing a switch towards an alternate M2 status. Understanding the IFP cellular heterogeneity and associated transcriptional programs may offer insights into novel therapeutic strategies for disabling joint disease pathologies.

scholarly journals Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing

Cells ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1751 ◽  
Author(s):  
Rishikesh Kumar Gupta ◽  
Jacek Kuznicki
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Future Directions ◽  
Strong Basis ◽  
Bodily Fluids ◽  
Single Cell Rna Sequencing

The present review discusses recent progress in single-cell RNA sequencing (scRNA-seq), which can describe cellular heterogeneity in various organs, bodily fluids, and pathologies (e.g., cancer and Alzheimer’s disease). We outline scRNA-seq techniques that are suitable for investigating cellular heterogeneity that is present in cell populations with very high resolution of the transcriptomic landscape. We summarize scRNA-seq findings and applications of this technology to identify cell types, activity, and other features that are important for the function of different bodily organs. We discuss future directions for scRNA-seq techniques that can link gene expression, protein expression, cellular function, and their roles in pathology. We speculate on how the field could develop beyond its present limitations (e.g., performing scRNA-seq in situ and in vivo). Finally, we discuss the integration of machine learning and artificial intelligence with cutting-edge scRNA-seq technology, which could provide a strong basis for designing precision medicine and targeted therapy in the future.

schex avoids overplotting for large single-cell RNA-sequencing datasets

2019 ◽  
Vol 36 (7) ◽  
pp. 2291-2292 ◽  
Author(s):  
Saskia Freytag ◽  
Ryan Lister
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Summary Due to the scale and sparsity of single-cell RNA-sequencing data, traditional plots can obscure vital information. Our R package schex overcomes this by implementing hexagonal binning, which has the additional advantages of improving speed and reducing storage for resulting plots. Availability and implementation schex is freely available from Bioconductor via http://bioconductor.org/packages/release/bioc/html/schex.html and its development version can be accessed on GitHub via https://github.com/SaskiaFreytag/schex. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Identification of diverse cell populations in skeletal muscles and biomarkers for intramuscular fat of chicken by single-cell RNA sequencing

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinghui Li ◽  
Siyuan Xing ◽  
Guiping Zhao ◽  
Maiqing Zheng ◽  
Xinting Yang ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Developmental Stage ◽  
Single Cell ◽  
Rna Sequencing ◽  
Muscle Development ◽  
Intramuscular Fat ◽  
Meat Production ◽  
Cell Clusters ◽  
Single Cell Rna Sequencing ◽  
Chicken Skeletal Muscle

Abstract Background The development of skeletal muscle is closely related to the efficiency of meat production and meat quality. Chicken skeletal muscle development depends on myogenesis and adipogenesis and occurs in two phases—hyperplasia and hypertrophy. However, cell profiles corresponding to the two-phase muscle development have yet to be determined. Single-cell RNA-sequencing (scRNA-seq) can elucidate the cell subpopulations in tissue and capture the gene expression of individual cells, which can provide new insights into the myogenesis and intramuscular adipogenesis. Results Ten cell clusters at the post-hatching developmental stage at Day 5 and seven cell clusters at the late developmental stage at Day 100 were identified in chicken breast muscles by scRNA-seq. Five myocyte-related clusters and two adipocyte clusters were identified at Day 5, and one myocyte cluster and one adipocyte cluster were identified at Day 100. The pattern of cell clustering varied between the two stages. The cell clusters showed clear boundaries at the terminal differentiation stage at Day 100; by contrast, cell differentiation was not complete at Day 5. APOA1 and COL1A1 were selected from up-regulated genes in the adipocyte cluster and found to be co-expressed with the ADIPOQ adipocyte marker gene in breast muscles by RNA in situ hybridization. Conclusions This study is the first to describe the heterogeneity of chicken skeletal muscle at two developmental stages. The genes APOA1 and COL1A1 were identified as biomarkers for chicken intramuscular fat cells.

scholarly journals Cellular heterogeneity of circulating CD4+CD8+ double-positive T cells characterized by single-cell RNA sequencing

2021 ◽  
Author(s):  
Sung Min Choi ◽  
Hi Jung Park ◽  
Eun A Choi ◽  
Kyeong Cheon Jung ◽  
Jae Il Lee
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cellular Heterogeneity ◽  
Flow Cytometry Analysis ◽  
Double Positive ◽  
Cynomolgus Monkeys ◽  
Disease States ◽  
Single Cell Rna Sequencing ◽  
Regulatory Functions

Abstract Circulating CD4+CD8+ double-positive (DP) T cells are associated with a variety of disease states. However, unlike conventional T cells, the composition of this population is poorly understood. Here, we used single-cell RNA sequencing (scRNA-seq) to analyze the composition and characteristics of the DP T cell population circulating in the peripheral blood of cynomolgus monkeys. We found that circulating DP T cells not only contain a large number of naïve cells, but also comprise a heterogeneous population (Th1-, Th2-, Th17-, Tfh-, Treg-, Eomes+ Tr1-, CD4 CTL-, CD8 CTL-, and innate-like cells) with multiple potential functions. Flow cytometry analysis revealed that a substantial number of the naïve DP T cells expressed CD8αβ, as well as CD8αα, along with high expression of CD31. Moreover, the CD4hiCD8lo and CD4hiCD8hi populations, which express high levels of the CD4 coreceptor, comprised subsets characterized by helper and regulatory functions, some of which also exhibited cytotoxic functions. By contrast, the CD4loCD8hi population with high CD8 coreceptor expression comprised a subset characterized by CD8 CTL- and innate-like properties. Taken together, the data show that scRNA-seq analysis identified a more diverse subset of the circulating DP cells than is currently known, despite this population being very small.

scholarly journals SIMPLEs: a single-cell RNA sequencing imputation strategy preserving gene modules and cell clusters variation

2020 ◽  
Author(s):  
Zhirui Hu ◽  
Songpeng Zu ◽  
Jun S. Liu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Stem Cell Differentiation ◽  
Marker Genes ◽  
Cell Type ◽  
Imputation Methods ◽  
Cell Clusters ◽  
Main Challenge ◽  
Single Cell Rna Sequencing ◽  
Gene Modules

AbstractA main challenge in analyzing single-cell RNA sequencing (scRNASeq) data is to reduce technical variations yet retain cell heterogeneity. Due to low mRNAs content per cell and molecule losses during the experiment (called “dropout”), the gene expression matrix has substantial zero read counts. Existing imputation methods either treat each cell or each gene identically and independently, which oversimplifies the gene correlation and cell type structure. We propose a statistical model-based approach, called SIMPLEs, which iteratively identifies correlated gene modules and cell clusters and imputes dropouts customized for individual gene module and cell type. Simultaneously, it quantifies the uncertainty of imputation and cell clustering. Optionally, SIMPLEs can integrate bulk RNASeq data for estimating dropout rates. In simulations, SIMPLEs performed significantly better than prevailing scRNASeq imputation methods by various metrics. By applying SIMPLEs to several real data sets, we discovered gene modules that can further classify subtypes of cells. Our imputations successfully recovered the expression trends of marker genes in stem cell differentiation and can discover putative pathways regulating biological processes.

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