scholarly journals Molecular Classification and Comparative Taxonomics of Foveal and Peripheral Cells in Primate Retina

2018 ◽  
Author(s):  
Yi-Rong Peng ◽  
Karthik Shekhar ◽  
Wenjun Yan ◽  
Dustin Herrmann ◽  
Anna Sappington ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Molecular Classification ◽  
Cell Types ◽  
Projection Neuron ◽  
Model Organisms ◽  
Peripheral Retina ◽  
Specific Expression ◽  
High Acuity ◽  
Retinal Region

ABSTRACTHigh acuity vision in primates, including humans, is mediated by a small central retinal region called the fovea. As more accessible model organisms lack a fovea, its specialized function and dysfunction in ocular diseases remain poorly understood. We used 165,000 single-cell RNA-seq profiles to generate and validate comprehensive cellular taxonomies of macaque fovea and peripheral retina. More than 80% of >65 cell types match between the two regions, but exhibit substantial differences in proportions and gene expression, some of which we relate to functional differences. Comparison of macaque retinal types with those of mice reveals that interneuron types are tightly conserved, but that projection neuron types and programs diverge, despite conserved transcription factor codes. Key macaque types are conserved in humans, allowing mapping of cell-type and region-specific expression of >190 genes associated with 6 human retinal diseases. Our work provides a framework for comparative single-cell analysis across tissue regions and species.

scholarly journals Enhancer viruses and a transgenic platform for combinatorial cell subclass-specific labeling

2019 ◽  
Author(s):  
Lucas T. Graybuck ◽  
Tanya L. Daigle ◽  
Adriana E. Sedeño-Cortés ◽  
Miranda Walker ◽  
Brian Kalmbach ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Types ◽  
Projection Neuron ◽  
Chromatin Accessibility ◽  
Specific Cell ◽  
Cell Type ◽  
Mouse Cortex ◽  
Efficient Access ◽  
Rapid Pace

SummaryThe rapid pace of cell type identification by new single-cell analysis methods has not been met with efficient experimental access to the newly discovered types. To enable flexible and efficient access to specific neural populations in the mouse cortex, we collected chromatin accessibility data from individual cells and clustered the single-cell data to identify enhancers specific for cell classes and subclasses. When cloned into adeno-associated viruses (AAVs) and delivered to the brain by retro-orbital injections, these enhancers drive transgene expression in specific cell subclasses in the cortex. We characterize several enhancer viruses in detail to show that they result in labeling of different projection neuron subclasses in mouse cortex, and that one of them can be used to label the homologous projection neuron subclass in human cortical slices. To enable the combinatorial labeling of more than one cell type by enhancer viruses, we developed a three-color Cre-, Flp- and Nigri-recombinase dependent reporter mouse line, Ai213. The delivery of three enhancer viruses driving these recombinases via a single retroorbital injection into a single Ai213 transgenic mouse results in labeling of three different neuronal classes/subclasses in the same brain tissue. This approach combines unprecedented flexibility with specificity for investigation of cell types in the mouse brain and beyond.

scholarly journals Single-cell analysis of human adipose tissue identifies depot- and disease-specific cell types

2019 ◽  
Vol 2 (1) ◽  
pp. 97-109 ◽  
Author(s):  
Jinchu Vijay ◽  
Marie-Frédérique Gauthier ◽  
Rebecca L. Biswell ◽  
Daniel A. Louiselle ◽  
Jeffrey J. Johnston ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Human Adipose Tissue ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Analysis ◽  
Disease Specific

scholarly journals Human Genes Escaping X-inactivation Revealed by Single Cell Expression Data

2018 ◽  
Author(s):  
Kerem Wainer Katsir ◽  
Michal Linial
Keyword(s):  
Single Cell ◽  
X Chromosome ◽  
Noncoding Rna ◽  
Phenotypic Variability ◽  
Cell Types ◽  
X Inactivation ◽  
Specific Expression ◽  
X Chromosomes ◽  
Human Genes ◽  
Cumulative Knowledge

AbstractBackgroundIn mammals, sex chromosomes pose an inherent imbalance of gene expression between sexes. In each female somatic cell, random inactivation of one of the X-chromosomes restores this balance. While most genes from the inactivated X-chromosome are silenced, 15-25% are known to escape X-inactivation (termed escapees). The expression levels of these genes are attributed to sex-dependent phenotypic variability.ResultsWe used single-cell RNA-Seq to detect escapees in somatic cells. As only one X-chromosome is inactivated in each cell, the origin of expression from the active or inactive chromosome can be determined from the variation of sequenced RNAs. We analyzed primary, healthy fibroblasts (n=104), and clonal lymphoblasts with sequenced parental genomes (n=25) by measuring the degree of allelic-specific expression (ASE) from heterozygous sites. We identified 24 and 49 candidate escapees, at varying degree of confidence, from the fibroblast and lymphoblast transcriptomes, respectively. We critically test the validity of escapee annotations by comparing our findings with a large collection of independent studies. We find that most genes (66%) from the unified set were previously reported as escapees. Furthermore, out of the overlooked escapees, 11 are long noncoding RNA (lncRNAs).ConclusionsX-chromosome inactivation and escaping from it are robust, permanent phenomena that are best studies at a single-cell resolution. The cumulative information from individual cells increases the potential of identifying escapees. Moreover, despite the use of a limited number of cells, clonal cells (i.e., same X-chromosomes are coordinately inhibited) with genomic phasing are valuable for detecting escapees at high confidence. Generalizing the method to uncharacterized genomic loci resulted in lncRNAs escapees which account for 20% of the listed candidates. By confirming genes as escapees and propose others as candidates from two different cell types, we contribute to the cumulative knowledge and reliability of human escapees.

scholarly journals Single-cell analysis revealed that IL4I1 promoted ovarian cancer progression

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Hongyu Zhao ◽  
Yu Teng ◽  
Wende Hao ◽  
Jie Li ◽  
Zhefeng Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Single Cell ◽  
Cancer Progression ◽  
Cox Regression ◽  
Single Cell Analysis ◽  
Cell Types ◽  
In Vitro Assays ◽  
Migration And Invasion ◽  
Dominant Type

Abstract Background Ovarian cancer was one of the leading causes of female deaths. Patients with OC were essentially incurable and portends a poor prognosis, presumably because of profound genetic heterogeneity limiting reproducible prognostic classifications. Methods We comprehensively analyzed an ovarian cancer single-cell RNA sequencing dataset, GSE118828, and identified nine major cell types. Relationship between the clusters was explored with CellPhoneDB. A malignant epithelial cluster was confirmed using pseudotime analysis, CNV and GSVA. Furthermore, we constructed the prediction model (i.e., RiskScore) consisted of 10 prognosis-specific genes from 2397 malignant epithelial genes using the LASSO Cox regression algorithm based on public datasets. Then, the prognostic value of Riskscore was assessed with Kaplan–Meier survival analysis and time-dependent ROC curves. At last, a series of in-vitro assays were conducted to explore the roles of IL4I1, an important gene in Riskscore, in OC progression. Results We found that macrophages possessed the most interaction pairs with other clusters, and M2-like TAMs were the dominant type of macrophages. C0 was identified as the malignant epithelial cluster. Patients with a lower RiskScore had a greater OS (log-rank P < 0.01). In training set, the AUC of RiskScore was 0.666, 0.743 and 0.809 in 1-year, 3-year and 5-year survival, respectively. This was also validated in another two cohorts. Moreover, downregulation of IL4I1 inhibited OC cells proliferation, migration and invasion. Conclusions Our work provide novel insights into our understanding of the heterogeneity among OCs, and would help elucidate the biology of OC and provide clinical guidance in prognosis for OC patients.

scholarly journals Single-cell analysis reveals cellular heterogeneity and molecular determinants of hypothalamic leptin-receptor cells

2020 ◽  
Author(s):  
N. Kakava-Georgiadou ◽  
J.F. Severens ◽  
A.M. Jørgensen ◽  
K.M. Garner ◽  
M.C.M Luijendijk ◽  
...  
Keyword(s):  
Single Cell ◽  
Leptin Receptor ◽  
Single Cell Analysis ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Molecular Signature ◽  
Neuronal Populations ◽  
Hypothalamic Nuclei ◽  
Satiety Hormone ◽  
Multiple Cell

AbstractHypothalamic nuclei which regulate homeostatic functions express leptin receptor (LepR), the primary target of the satiety hormone leptin. Single-cell RNA sequencing (scRNA-seq) has facilitated the discovery of a variety of hypothalamic cell types. However, low abundance of LepR transcripts prevented further characterization of LepR cells. Therefore, we perform scRNA-seq on isolated LepR cells and identify eight neuronal clusters, including three uncharacterized Trh-expressing populations as well as 17 non-neuronal populations including tanycytes, oligodendrocytes and endothelial cells. Food restriction had a major impact on Agrp neurons and changed the expression of obesity-associated genes. Multiple cell clusters were enriched for GWAS signals of obesity. We further explored changes in the gene regulatory landscape of LepR cell types. We thus reveal the molecular signature of distinct populations with diverse neurochemical profiles, which will aid efforts to illuminate the multi-functional nature of leptin’s action in the hypothalamus.

scholarly journals Single cell analysis reveals immune cell–adipocyte crosstalk regulating the transcription of thermogenic adipocytes

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Prashant Rajbhandari ◽  
Douglas Arneson ◽  
Sydney K Hart ◽  
In Sook Ahn ◽  
Graciel Diamante ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Single Cell Analysis ◽  
Metabolic Response ◽  
Cell Types ◽  
Specific Cell ◽  
Beneficial Effects ◽  
Thermogenic Adipocytes ◽  
And Function

Immune cells are vital constituents of the adipose microenvironment that influence both local and systemic lipid metabolism. Mice lacking IL10 have enhanced thermogenesis, but the roles of specific cell types in the metabolic response to IL10 remain to be defined. We demonstrate here that selective loss of IL10 receptor α in adipocytes recapitulates the beneficial effects of global IL10 deletion, and that local crosstalk between IL10-producing immune cells and adipocytes is a determinant of thermogenesis and systemic energy balance. Single Nuclei Adipocyte RNA-sequencing (SNAP-seq) of subcutaneous adipose tissue defined a metabolically-active mature adipocyte subtype characterized by robust expression of genes involved in thermogenesis whose transcriptome was selectively responsive to IL10Rα deletion. Furthermore, single-cell transcriptomic analysis of adipose stromal populations identified lymphocytes as a key source of IL10 production in response to thermogenic stimuli. These findings implicate adaptive immune cell-adipocyte communication in the maintenance of adipose subtype identity and function.

scholarly journals Methods and sensors for functional genomic studies of cell-cycle transitions in single cells

2020 ◽  
Vol 52 (10) ◽  
pp. 468-477
Author(s):  
Alexander C. Zambon ◽  
Tom Hsu ◽  
Seunghee Erin Kim ◽  
Miranda Klinck ◽  
Jennifer Stowe ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Imaging System ◽  
Single Cells ◽  
Cell Types ◽  
Cell Analysis ◽  
Mcf7 Cells ◽  
Genomic Studies ◽  
Cell Subpopulations

Much of our understanding of the regulatory mechanisms governing the cell cycle in mammals has relied heavily on methods that measure the aggregate state of a population of cells. While instrumental in shaping our current understanding of cell proliferation, these approaches mask the genetic signatures of rare subpopulations such as quiescent (G0) and very slowly dividing (SD) cells. Results described in this study and those of others using single-cell analysis reveal that even in clonally derived immortalized cancer cells, ∼1–5% of cells can exhibit G0 and SD phenotypes. Therefore to enable the study of these rare cell phenotypes we established an integrated molecular, computational, and imaging approach to track, isolate, and genetically perturb single cells as they proliferate. A genetically encoded cell-cycle reporter (K67p-FUCCI) was used to track single cells as they traversed the cell cycle. A set of R-scripts were written to quantify K67p-FUCCI over time. To enable the further study G0 and SD phenotypes, we retrofitted a live cell imaging system with a micromanipulator to enable single-cell targeting for functional validation studies. Single-cell analysis revealed HT1080 and MCF7 cells had a doubling time of ∼24 and ∼48 h, respectively, with high duration variability in G1 and G2 phases. Direct single-cell microinjection of mRNA encoding (GFP) achieves detectable GFP fluorescence within ∼5 h in both cell types. These findings coupled with the possibility of targeting several hundreds of single cells improves throughput and sensitivity over conventional methods to study rare cell subpopulations.

scholarly journals Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Qingnan Liang ◽  
Rachayata Dharmat ◽  
Leah Owen ◽  
Akbar Shakoor ◽  
Yumei Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Retinal Cell ◽  
Peripheral Retina ◽  
Marker Genes ◽  
Rna Seq ◽  
Cell Type ◽  
Retinal Tissue ◽  
The Individual

AbstractSingle-cell RNA-seq is a powerful tool in decoding the heterogeneity in complex tissues by generating transcriptomic profiles of the individual cell. Here, we report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human retinal tissue, which is composed of multiple cell types with distinct functions. Six samples from three healthy donors are profiled and high-quality RNA-seq data is obtained for 5873 single nuclei. All major retinal cell types are observed and marker genes for each cell type are identified. The gene expression of the macular and peripheral retina is compared to each other at cell-type level. Furthermore, our dataset shows an improved power for prioritizing genes associated with human retinal diseases compared to both mouse single-cell RNA-seq and human bulk RNA-seq results. In conclusion, we demonstrate that obtaining single cell transcriptomes from human frozen tissues can provide insight missed by either human bulk RNA-seq or animal models.

scholarly journals Stratification of chemotherapy-treated stage III colorectal cancer patients using multiplexed imaging and single-cell analysis of T-cell populations

2021 ◽  
Author(s):  
Xanthi Stachtea ◽  
Maurice B. Loughrey ◽  
Manuela Salvucci ◽  
Andreas U. Lindner ◽  
Sanghee Cho ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Single Cell ◽  
Immune Cell ◽  
Single Cell Analysis ◽  
Cell Types ◽  
Cell Segmentation ◽  
Stage Iii ◽  
Cell Analysis ◽  
Increased Risk

AbstractColorectal cancer (CRC) has one of the highest cancer incidences and mortality rates. In stage III, postoperative chemotherapy benefits <20% of patients, while more than 50% will develop distant metastases. Biomarkers for identification of patients at increased risk of disease recurrence following adjuvant chemotherapy are currently lacking. In this study, we assessed immune signatures in the tumor and tumor microenvironment (TME) using an in situ multiplexed immunofluorescence imaging and single-cell analysis technology (Cell DIVETM) and evaluated their correlations with patient outcomes. Tissue microarrays (TMAs) with up to three 1 mm diameter cores per patient were prepared from 117 stage III CRC patients treated with adjuvant fluoropyrimidine/oxaliplatin (FOLFOX) chemotherapy. Single sections underwent multiplexed immunofluorescence staining for immune cell markers (CD45, CD3, CD4, CD8, FOXP3, PD1) and tumor/cell segmentation markers (DAPI, pan-cytokeratin, AE1, NaKATPase, and S6). We used annotations and a probabilistic classification algorithm to build statistical models of immune cell types. Images were also qualitatively assessed independently by a Pathologist as ‘high’, ‘moderate’ or ‘low’, for stromal and total immune cell content. Excellent agreement was found between manual assessment and total automated scores (p < 0.0001). Moreover, compared to single markers, a multi-marker classification of regulatory T cells (Tregs: CD3+/CD4+FOXP3+/PD1−) was significantly associated with disease-free survival (DFS) and overall survival (OS) (p = 0.049 and 0.032) of FOLFOX-treated patients. Our results also showed that PD1− Tregs rather than PD1+ Tregs were associated with improved survival. These findings were supported by results from an independent FOLFOX-treated cohort of 191 stage III CRC patients, where higher PD1− Tregs were associated with an increase overall survival (p = 0.015) for CD3+/CD4+/FOXP3+/PD1−. Overall, compared to single markers, multi-marker classification provided more accurate quantitation of immune cell types with stronger correlations with outcomes.

scholarly journals Automated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry

2021 ◽  
Vol 9 ◽  
Author(s):  
Cindy X. Chen ◽  
Han Sang Park ◽  
Hillel Price ◽  
Adam Wax
Keyword(s):  
Single Cell ◽  
Cancer Cells ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Imaging Modality ◽  
Cell Types ◽  
Needle Aspiration ◽  
Microfluidic Channels ◽  
Phase Imaging ◽  
Imaging Data

Holographic cytometry is an ultra-high throughput quantitative phase imaging modality that is capable of extracting subcellular information from millions of cells flowing through parallel microfluidic channels. In this study, we present our findings on the application of holographic cytometry to distinguishing carcinogen-exposed cells from normal cells and cancer cells. This has potential application for environmental monitoring and cancer detection by analysis of cytology samples acquired via brushing or fine needle aspiration. By leveraging the vast amount of cell imaging data, we are able to build single-cell-analysis-based biophysical phenotype profiles on the examined cell lines. Multiple physical characteristics of these cells show observable distinct traits between the three cell types. Logistic regression analysis provides insight on which traits are more useful for classification. Additionally, we demonstrate that deep learning is a powerful tool that can potentially identify phenotypic differences from reconstructed single-cell images. The high classification accuracy levels show the platform’s potential in being developed into a diagnostic tool for abnormal cell screening.

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