scholarly journals Chemical, molecular, and single cell analysis reveal chondroitin sulfate proteoglycan aberrancy in fibrolamellar carcinoma

2021 ◽  
Author(s):  
Adam Francisco ◽  
Jine Li ◽  
Alaa Farghli ◽  
Matt Kanke ◽  
Bo Shui ◽  
...  
Keyword(s):  
Single Cell ◽  
Potential Role ◽  
Single Cell Analysis ◽  
Cell Types ◽  
Different Types ◽  
Associated Proteins ◽  
Fibrolamellar Carcinoma ◽  
Malignant Liver ◽  
Extracellular Environment ◽  
Accessible Chromatin

Fibrolamellar carcinoma (FLC) is an aggressive liver cancer with no effective therapeutic options. The extracellular environment of FLC tumors is poorly characterized and may contribute to cancer growth and/or metastasis. To bridge this knowledge gap, we assessed pathways relevant to proteoglycans, a major component of the extracellular matrix. We first analyzed gene expression data from FLC and non-malignant liver tissue to identify changes in glycosaminoglycan (GAG) biosynthesis pathways. We then implemented a novel LC-MS/MS based method to quantify the abundance of different types of GAGs in patient tumors, followed by measurement of the levels of different GAG-associated proteins. Finally, we performed the first single-cell assay for transposon-accessible chromatin-sequencing on FLC tumors, to identify which cell types are linked to the most dominant GAG-associated protein in FLC. Our results reveal a pathologic aberrancy in chondroitin (but not heparan) sulfate proteoglycans in FLC and highlight a potential role for activated stellate cells.

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

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.

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 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 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 Enhancement and Imputation of Peak Signal Enables Accurate Cell-Type Classification in scATAC-seq

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhe Cui ◽  
Ya Cui ◽  
Yan Gao ◽  
Tao Jiang ◽  
Tianyi Zang ◽  
...  
Keyword(s):  
Single Cell ◽  
Mononuclear Cells ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Support Vector ◽  
Cell Type ◽  
Peripheral Blood Mononuclear ◽  
Copy Numbers ◽  
Genome Wide ◽  
Accessible Chromatin

Single-cell Assay Transposase Accessible Chromatin sequencing (scATAC-seq) has been widely used in profiling genome-wide chromatin accessibility in thousands of individual cells. However, compared with single-cell RNA-seq, the peaks of scATAC-seq are much sparser due to the lower copy numbers (diploid in humans) and the inherent missing signals, which makes it more challenging to classify cell type based on specific expressed gene or other canonical markers. Here, we present svmATAC, a support vector machine (SVM)-based method for accurately identifying cell types in scATAC-seq datasets by enhancing peak signal strength and imputing signals through patterns of co-accessibility. We applied svmATAC to several scATAC-seq data from human immune cells, human hematopoietic system cells, and peripheral blood mononuclear cells. The benchmark results showed that svmATAC is free of literature-based markers and robust across datasets in different libraries and platforms. The source code of svmATAC is available at https://github.com/mrcuizhe/svmATAC under the MIT license.

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.

scholarly journals Novel microfluidic platform accelerates processing of tissue into single cells for molecular analysis and primary culture models

2020 ◽  
Author(s):  
Jeremy Lombardo ◽  
Marzieh Aliaghaei ◽  
Quy Nguyen ◽  
Kai Kessenbrock ◽  
Jered Haun
Keyword(s):  
Single Cell ◽  
Stress Responses ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Types ◽  
Tissue Preparation ◽  
Cell Analysis ◽  
Processing Technologies ◽  
Microfluidic Platform ◽  
Wide Range

Abstract Tissues are composed of highly heterogeneous mixtures of cell subtypes, and this diversity is increasingly being characterized using high-throughput single cell analysis methods. However, these efforts are hindered by the fact that tissues must first be dissociated into single cell suspensions that are viable and still accurately represent phenotypes from the original tissue. Current methods for breaking down tissues are inefficient, labor-intensive, subject to high variability, and potentially biased towards cell subtypes that are easier to release. Here, we present a microfluidic platform consisting of three different tissue processing technologies that can perform the complete tissue to single cell workflow, including digestion, disaggregation, and filtration. First, we developed a new microfluidic digestion device that can be loaded with minced tissue specimens quickly and easily, and then use the combination of proteolytic enzyme activity and fluid shear forces to accelerate tissue breakdown. Next, we integrated dissociation and filter technologies into a single device, which enhanced single cell numbers and fully prepared the sample for single cell analysis. The final multi-device platform was then evaluated using a diverse array of tissue types that exhibited a wide range of properties. For murine kidney and mammary tumor, we found that microfluidic processing produced 2.5-fold more single, viable cells. Single cell RNA sequencing (scRNA-seq) further revealed that device processing enriched for endothelial cells, fibroblasts, and basal epithelium, and did not increase stress responses. For murine liver and heart, which are softer tissues containing fragile cell types, processing time could be reduced to 15 min, and even as short as 1 min. We also demonstrated that periodic recovery at defined time intervals produced substantially more hepatocytes and cardiomyocytes than continuous operation, most likely by preventing damage to fragile cell types. In future work, we will seek to integrate additional operations such as upstream tissue preparation and downstream microfluidic cell sorting and detection to create powerful point-of-care single cell diagnostic platforms.

Sign in / Sign up

Export Citation Format

Share Document