Tissue distribution of primary metabolism between epidermal, mesophyll and parenchymatous bundle sheath cells in barley leaves

This paper originates from a presentation at the International Conference on Assimilate Transport and Partitioning, Newcastle, NSW, August 1999 In order to investigate the roles of different cell types, metabolite compartmentation in barley (Hordeum vulgare L.) leaf tissue was mapped at the single-cell level, using single-cell sampling and analysis (SiCSA) techniques. The partitioning of recently fixed photoassimilate was investigated for the first time at single-cell resolution, using BAMS (biological accelerator mass spectroscopy) for precise measurement of 14C in femtomole quantities. The data obtained by BAMS qualitatively reflect concentrations of sugars in different cell types measured by SiCSA. Calculation of 14C-specific activities showed that the radioactive label saturated the mesophyll and parenchymatous bundle sheath (PBS) pools within the 45-min labelling period. During the photoperiod, sucrose concentration increased to 200 mM in mesophyll cells. The concentration of malate also increased during the photoperiod in mesophyll and PBS cells. Epidermal cells contained very low concentrations of sugar but high concentrations of malate (120–180 mM) and did not show significant diurnal changes. Accumulation of sugars and fructan synthesis could be induced in mesophyll and PBS cells by reduced export of sugars from leaves or, alternatively, when sugars were supplied from excised leaf blade bases immersed in a sucrose solution in the dark. The epidermis accumulated additional malate in step with the accumulation of sugar by the mesophyll/PBS cells during the long-term reduction of export. Immunolocalisation of Rubisco and cytochrome oxidase proteins was used to analyse the distribution of enzymes of photoassimilation and respiration between functionally different cells in mature leaves of barley.

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A fate-mapping approach reveals the composite origin of the connecting tubule and alerts on “single-cell”-specific KO model of the distal nephron

AJP Renal Physiology ◽

10.1152/ajprenal.00286.2016 ◽

2016 ◽

Vol 311 (5) ◽

pp. F901-F906 ◽

Cited By ~ 22

Author(s):

Francesco Trepiccione ◽

Christelle Soukaseum ◽

Anna Iervolino ◽

Federica Petrillo ◽

Miriam Zacchia ◽

...

Keyword(s):

Single Cell ◽

Kidney Development ◽

Fluorescent Protein ◽

Collecting Duct ◽

Yellow Fluorescent Protein ◽

Cell Types ◽

Intercalated Cells ◽

Distal Nephron ◽

Principal Cells ◽

Different Cell Types

The distal nephron is a heterogeneous part of the nephron composed by six different cell types, forming the epithelium of the distal convoluted (DCT), connecting, and collecting duct. To dissect the function of these cells, knockout models specific for their unique cell marker have been created. However, since this part of the nephron develops at the border between the ureteric bud and the metanephric mesenchyme, the specificity of the single cell markers has been recently questioned. Here, by mapping the fate of the aquaporin 2 (AQP2) and Na+-Cl−cotransporter (NCC)-positive cells using transgenic mouse lines expressing the yellow fluorescent protein fluorescent marker, we showed that the origin of the distal nephron is extremely composite. Indeed, AQP2-expressing precursor results give rise not only to the principal cells, but also to some of the A- and B-type intercalated cells and even to cells of the DCT. On the other hand, some principal cells and B-type intercalated cells can develop from NCC-expressing precursors. In conclusion, these results demonstrate that the origin of different cell types in the distal nephron is not as clearly defined as originally thought. Importantly, they highlight the fact that knocking out a gene encoding for a selective functional marker in the adult does not guarantee cell specificity during the overall kidney development. Tools allowing not only cell-specific but also time-controlled recombination will be useful in this sense.

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Single-cell transcriptome analysis of tumor and stromal compartments of pancreatic ductal adenocarcinoma primary tumors and metastatic lesions

Genome Medicine ◽

10.1186/s13073-020-00776-9 ◽

2020 ◽

Vol 12 (1) ◽

Author(s):

Wei Lin ◽

Pawan Noel ◽

Erkut H. Borazanci ◽

Jeeyun Lee ◽

Albert Amini ◽

...

Keyword(s):

Single Cell ◽

Tumor Cells ◽

Cell Types ◽

Ductal Adenocarcinoma ◽

Cellular Composition ◽

Primary Tumors ◽

Metastatic Lesions ◽

Tumor Tissues ◽

Cell Type Specific ◽

Different Cell Types

Abstract Background Solid tumors such as pancreatic ductal adenocarcinoma (PDAC) comprise not just tumor cells but also a microenvironment with which the tumor cells constantly interact. Detailed characterization of the cellular composition of the tumor microenvironment is critical to the understanding of the disease and treatment of the patient. Single-cell transcriptomics has been used to study the cellular composition of different solid tumor types including PDAC. However, almost all of those studies used primary tumor tissues. Methods In this study, we employed a single-cell RNA sequencing technology to profile the transcriptomes of individual cells from dissociated primary tumors or metastatic biopsies obtained from patients with PDAC. Unsupervised clustering analysis as well as a new supervised classification algorithm, SuperCT, was used to identify the different cell types within the tumor tissues. The expression signatures of the different cell types were then compared between primary tumors and metastatic biopsies. The expressions of the cell type-specific signature genes were also correlated with patient survival using public datasets. Results Our single-cell RNA sequencing analysis revealed distinct cell types in primary and metastatic PDAC tissues including tumor cells, endothelial cells, cancer-associated fibroblasts (CAFs), and immune cells. The cancer cells showed high inter-patient heterogeneity, whereas the stromal cells were more homogenous across patients. Immune infiltration varies significantly from patient to patient with majority of the immune cells being macrophages and exhausted lymphocytes. We found that the tumor cellular composition was an important factor in defining the PDAC subtypes. Furthermore, the expression levels of cell type-specific markers for EMT+ cancer cells, activated CAFs, and endothelial cells significantly associated with patient survival. Conclusions Taken together, our work identifies significant heterogeneity in cellular compositions of PDAC tumors and between primary tumors and metastatic lesions. Furthermore, the cellular composition was an important factor in defining PDAC subtypes and significantly correlated with patient outcome. These findings provide valuable insights on the PDAC microenvironment and could potentially inform the management of PDAC patients.

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Cell-type-specific analysis of alternative polyadenylation using single-cell transcriptomics data

Nucleic Acids Research ◽

10.1093/nar/gkz781 ◽

2019 ◽

Vol 47 (19) ◽

pp. 10027-10039 ◽

Cited By ~ 11

Author(s):

Eldad David Shulman ◽

Ran Elkon

Keyword(s):

Gene Regulation ◽

Single Cell ◽

Alternative Polyadenylation ◽

Cell Types ◽

Protein Coding ◽

Multiple Datasets ◽

Transcriptomics Data ◽

Cell Type Specific ◽

Transcriptional Gene Regulation ◽

Different Cell Types

AbstractAlternative polyadenylation (APA) is emerging as an important layer of gene regulation because the majority of mammalian protein-coding genes contain multiple polyadenylation (pA) sites in their 3′ UTR. By alteration of 3′ UTR length, APA can considerably affect post-transcriptional gene regulation. Yet, our understanding of APA remains rudimentary. Novel single-cell RNA sequencing (scRNA-seq) techniques allow molecular characterization of different cell types to an unprecedented degree. Notably, the most popular scRNA-seq protocols specifically sequence the 3′ end of transcripts. Building on this property, we implemented a method for analysing patterns of APA regulation from such data. Analyzing multiple datasets from diverse tissues, we identified widespread modulation of APA in different cell types resulting in global 3′ UTR shortening/lengthening and enhanced cleavage at intronic pA sites. Our results provide a proof-of-concept demonstration that the huge volume of scRNA-seq data that accumulates in the public domain offers a unique resource for the exploration of APA based on a very broad collection of cell types and biological conditions.

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Molecular comparison of single cell MDA products derived from different cell types

Reproductive BioMedicine Online ◽

10.1016/s1472-6483(10)60051-x ◽

2009 ◽

Vol 19 (1) ◽

pp. 89-98 ◽

Cited By ~ 18

Author(s):

Stavros Glentis ◽

Sioban SenGupta ◽

Alan Thornhill ◽

Rubin Wang ◽

Ian Craft ◽

...

Keyword(s):

Single Cell ◽

Cell Types ◽

Molecular Comparison ◽

Different Cell Types

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scAPAdb: a comprehensive database of alternative polyadenylation at single-cell resolution

Nucleic Acids Research ◽

10.1093/nar/gkab795 ◽

2021 ◽

Author(s):

Sheng Zhu ◽

Qiwei Lian ◽

Wenbin Ye ◽

Wei Qin ◽

Zhe Wu ◽

...

Keyword(s):

Single Cell ◽

Alternative Polyadenylation ◽

Cell Types ◽

Single Cell Level ◽

Cell Heterogeneity ◽

Rna Seq ◽

Cell Level ◽

Eukaryotic Gene ◽

User Friendly ◽

Different Cell Types

Abstract Alternative polyadenylation (APA) is a widespread regulatory mechanism of transcript diversification in eukaryotes, which is increasingly recognized as an important layer for eukaryotic gene expression. Recent studies based on single-cell RNA-seq (scRNA-seq) have revealed cell-to-cell heterogeneity in APA usage and APA dynamics across different cell types in various tissues, biological processes and diseases. However, currently available APA databases were all collected from bulk 3′-seq and/or RNA-seq data, and no existing database has provided APA information at single-cell resolution. Here, we present a user-friendly database called scAPAdb (http://www.bmibig.cn/scAPAdb), which provides a comprehensive and manually curated atlas of poly(A) sites, APA events and poly(A) signals at the single-cell level. Currently, scAPAdb collects APA information from > 360 scRNA-seq experiments, covering six species including human, mouse and several other plant species. scAPAdb also provides batch download of data, and users can query the database through a variety of keywords such as gene identifier, gene function and accession number. scAPAdb would be a valuable and extendable resource for the study of cell-to-cell heterogeneity in APA isoform usages and APA-mediated gene regulation at the single-cell level under diverse cell types, tissues and species.

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Computational approaches towards reducing contamination in single-cell RNA-seq data

10.1101/2020.07.15.205062 ◽

2020 ◽

Author(s):

Siamak Yousefi ◽

Hao Chen ◽

Jesse F. Ingels ◽

Melinda S. McCarty ◽

Arthur G. Centeno ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Real Life ◽

Cell Types ◽

Cell Capture ◽

Rna Seq ◽

Sequence Analyses ◽

Cell Functions ◽

Biological Interpretation ◽

Different Cell Types

SUMMARYSingle cell RNA sequencing has enabled quantification of single cells and identification of different cell types and subtypes as well as cell functions in different tissues. Single cell RNA sequence analyses assume acquired RNAs correspond to cells, however, RNAs from contamination within the input data are also captured by these assays. The sequencing of background contamination as well as unwanted cells making their way to the final assay Potentially confound the correct biological interpretation of single cell transcriptomic data. Here we demonstrate two approaches to deal with background contamination as well as profiling of unwanted cells in the assays. We use three real-life datasets of whole-cell capture and nucleotide single-cell captures generated by Fluidigm and 10x technologies and show that these methods reduce the effect of contamination, strengthen clustering of cells and improves biological interpretation.

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Mapping multicellular programs from single-cell profiles

10.1101/2020.08.11.245472 ◽

2020 ◽

Author(s):

Livnat Jerby-Arnon ◽

Aviv Regev

Keyword(s):

Single Cell ◽

Spatial Data ◽

Spatial Information ◽

Single Cells ◽

Cell Types ◽

Risk Genes ◽

Health And Disease ◽

Different Cell Types ◽

Cellular Components ◽

Acting In Concert

ABSTRACTTissue homeostasis relies on orchestrated multicellular circuits, where interactions between different cell types dynamically balance tissue function. While single-cell genomics identifies tissues’ cellular components, deciphering their coordinated action remains a major challenge. Here, we tackle this problem through a new framework of multicellular programs: combinations of distinct cellular programs in different cell types that are coordinated together in the tissue, thus forming a higher order functional unit at the tissue, rather than only cell, level. We develop the open-access DIALOGUE algorithm to systematically uncover such multi-cellular programs not only from spatial data, but even from tissue dissociated and profiled as single cells, e.g., by single-cell RNA-Seq. Tested on spatial transcriptomes from the mouse hypothalamus, DIALOGUE recovered spatial information, predicted the properties of a cell’s environment only based on its transcriptome, and identified multicellular programs that mark animal behavior. Applied to brain samples and colon biopsies profiled by scRNA-Seq, DIALOGUE identified multicellular configurations that mark Alzheimer’s disease and ulcerative colitis (UC), including a program spanning five cell types that is predictive of response to anti-TNF therapy in UC patients and enriched for UC risk genes from GWAS, each acting in different cell types, but all cells acting in concert. Taken together, our study provides a novel conceptual and methodological framework to unravel multicellular regulation in health and disease.

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Single-cell RNA-Seq Reveals Transcriptional Landscape and Intra-tumor Heterogenicity in Gallbladder Cancer Liver Metastasis Microenvironment

10.21203/rs.3.rs-117830/v1 ◽

2020 ◽

Author(s):

Wenhua You ◽

Xiangyu Li ◽

Peng Wang ◽

Bowen Sha ◽

Yuan Liang ◽

...

Keyword(s):

T Cells ◽

Liver Metastasis ◽

Gallbladder Cancer ◽

Single Cell ◽

Molecular Mechanisms ◽

Cell Types ◽

Malignant Cells ◽

Different Cell Types ◽

High Degree

Abstract Background: Gallbladder cancer (GBC) is a highly aggressive biliary epithelial malignancy. Tumor invasion and metastasis contributed to the high mortality of GBC patients. However, molecular mechanisms involved in GBC metastases are still little known. Methods: We performed single-cell RNA sequencing on GBC liver metastasis tissue and analyzed the data based on different cell types.Results: In this study, 8 cell types, including T cells, B cells, malignant cells, fibroblasts, endothelial cells, macrophages, dendritic cells, and mast cells were identified. Malignant cells displayed a high degree of intra-tumor heterogenicity and neutrophils could promote GBC progression in vitro. Besides, cytotoxic CD8+ T cells became exhausted and CD4+ Tregs presented immunosuppressive characteristics. Macrophages played an important role in the tumor microenvironment. We identified three distinct macrophage subsets and emerged M2 polarization. We also found that cancer-associated fibroblasts exhibited heterogeneity and promoted GBC metastasis. Conclusions: In conclusion, our work provided a landscape view at the single-cell level and may clear the way for the therapy of GBC metastases.

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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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Massively multiplex single-cell Hi-C

10.1101/065052 ◽

2016 ◽

Cited By ~ 4

Author(s):

Vijay Ramani ◽

Xinxian Deng ◽

Kevin L Gunderson ◽

Frank J Steemers ◽

Christine M Disteche ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Cell Types ◽

Cell Heterogeneity ◽

Proof Of Concept ◽

Chromosome Conformation ◽

Large Numbers ◽

Conformational Properties ◽

Novel Method ◽

Different Cell Types

AbstractWe present combinatorial single cell Hi-C, a novel method that leverages combinatorial cellular indexing to measure chromosome conformation in large numbers of single cells. In this proof-of-concept, we generate and sequence combinatorial single cell Hi-C libraries for two mouse and four human cell types, comprising a total of 9,316 single cells across 5 experiments. We demonstrate the utility of single-cell Hi-C data in separating different cell types, identify previously uncharacterized cell-to-cell heterogeneity in the conformational properties of mammalian chromosomes, and demonstrate that combinatorial indexing is a generalizable molecular strategy for single-cell genomics.

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