scholarly journals A cluster-aware, weighted ensemble clustering method for cell-type detection

2018 ◽  
Author(s):  
Daphne Tsoucas ◽  
Guo-Cheng Yuan
Keyword(s):  
Single Cell Analysis ◽  
Fano Factor ◽  
Cell Types ◽  
Large Datasets ◽  
Computational Method ◽  
Cell Analysis ◽  
Ensemble Clustering ◽  
Cell Type ◽  
Very Large Datasets ◽  
Common Cell

ABSTRACTSingle-cell analysis is a powerful tool for dissecting the cellular composition within a tissue or organ. However, it remains difficult to detect rare and common cell types at the same time. Here we present a new computational method, called GiniClust2, to overcome this challenge. GiniClust2 combines the strengths of two complementary approaches, using the Gini index and Fano factor, respectively, through a cluster-aware, weighted ensemble clustering technique. GiniClust2 successfully identifies both common and rare cell types in diverse datasets, outperforming existing methods. GiniClust2 is scalable to very large datasets.

scholarly journals EMeth: An EM algorithm for cell type decomposition based on DNA methylation data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hanyu Zhang ◽  
Ruoyi Cai ◽  
James Dai ◽  
Wei Sun
Keyword(s):  
Dna Methylation ◽  
Tumor Cells ◽  
T Regulatory Cells ◽  
Simulated Data ◽  
Cell Types ◽  
Computational Method ◽  
Methylation Data ◽  
Cell Type ◽  
A Cell ◽  
Type Decomposition

AbstractWe introduce a new computational method named EMeth to estimate cell type proportions using DNA methylation data. EMeth is a reference-based method that requires cell type-specific DNA methylation data from relevant cell types. EMeth improves on the existing reference-based methods by detecting the CpGs whose DNA methylation are inconsistent with the deconvolution model and reducing their contributions to cell type decomposition. Another novel feature of EMeth is that it allows a cell type with known proportions but unknown reference and estimates its methylation. This is motivated by the case of studying methylation in tumor cells while bulk tumor samples include tumor cells as well as other cell types such as infiltrating immune cells, and tumor cell proportion can be estimated by copy number data. We demonstrate that EMeth delivers more accurate estimates of cell type proportions than several other methods using simulated data and in silico mixtures. Applications in cancer studies show that the proportions of T regulatory cells estimated by DNA methylation have expected associations with mutation load and survival time, while the estimates from gene expression miss such associations.

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 A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification

2018 ◽  
Author(s):  
Douglas Abrams ◽  
Parveen Kumar ◽  
R. Krishna Murthy Karuturi ◽  
Joshy George
Keyword(s):  
Experimental Design ◽  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Cell Number ◽  
Fold Change ◽  
Computational Method ◽  
Marker Genes ◽  
Cell Type ◽  
Estimate Sample Size

AbstractBackgroundThe advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification.ResultsWe have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment.ConclusionsBased on our study, we found that when marker genes are expressed at fold change of 4 or more than the rest of the genes, either Seurat or Simlr algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fC 2 upregulated, choice of the single cell algorithm is dependent on the number of single cells isolated and proportion of rare cell type to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in examining the single cell experimental design.

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 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.

scholarly journals Single Cell Analysis of ACE2 Expression Reveals the Potential Targets for 2019-nCoV

2020 ◽  
Author(s):  
Yanyan Zhu ◽  
Miaomiao Jiang ◽  
Liang Gao ◽  
Xiaoyun Huang
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Types ◽  
Cell Entry ◽  
The Novel ◽  
Cell Analysis ◽  
Systematic Analysis ◽  
Proximal Tubule Cells ◽  
Putative Receptor ◽  
Novel Coronavirus

ACE2, the putative receptor for the novel coronavirus (2019-nCoV), played an important role in cell entry of 2019-nCoV. However, it is not yet clear what cell types within the human body express ACE2. Here, a systematic analysis was undertaken using published single cell datasets. In total, our study analyzed 229652 cells, from five different organs, derived from 88 donors. The top ACE2 expressing cells include proximal tubule cells in the kidney and enterocytes in the intestine. Other major ACE2 expressing cells in the kidney include podocytes, intercalated cells and endothelial cells. Our results offer a comprehensive atlas of ACE2 expression at the single cell level and unravel the enormous potential targets of 2019-nCoVinfection beyond the lung.

scholarly journals Common cell type nomenclature for the mammalian brain

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Jeremy A Miller ◽  
Nathan W Gouwens ◽  
Bosiljka Tasic ◽  
Forrest Collman ◽  
Cindy TJ van Velthoven ◽  
...  
Keyword(s):  
Cell Types ◽  
Data Driven ◽  
Mammalian Brain ◽  
Gene Transcript ◽  
Cell Type ◽  
Sequencing Technologies ◽  
Multiple Organs ◽  
Source Data ◽  
The Common ◽  
Common Cell

The advancement of single-cell RNA-sequencing technologies has led to an explosion of cell type definitions across multiple organs and organisms. While standards for data and metadata intake are arising, organization of cell types has largely been left to individual investigators, resulting in widely varying nomenclature and limited alignment between taxonomies. To facilitate cross-dataset comparison, the Allen Institute created the common cell type nomenclature (CCN) for matching and tracking cell types across studies that is qualitatively similar to gene transcript management across different genome builds. The CCN can be readily applied to new or established taxonomies and was applied herein to diverse cell type datasets derived from multiple quantifiable modalities. The CCN facilitates assigning accurate yet flexible cell type names in the mammalian cortex as a step toward community-wide efforts to organize multi-source, data-driven information related to cell type taxonomies from any organism.

scholarly journals The prevalence and origin of exoprotease-producing cells in the Bacillus subtilis biofilm

Microbiology ◽  
2014 ◽  
Vol 160 (1) ◽  
pp. 56-66 ◽  
Author(s):  
Victoria L. Marlow ◽  
Francesca R. Cianfanelli ◽  
Michael Porter ◽  
Lynne S. Cairns ◽  
J. Kim Dale ◽  
...  
Keyword(s):  
Bacillus Subtilis ◽  
Biofilm Formation ◽  
Single Cell Analysis ◽  
Response Regulator ◽  
Cell Types ◽  
Cell Analysis ◽  
Swarming Motility ◽  
Specialized Cell ◽  
Differentiated Cells ◽  
First Time

Biofilm formation by the Gram-positive bacterium Bacillus subtilis is tightly controlled at the level of transcription. The biofilm contains specialized cell types that arise from controlled differentiation of the resident isogenic bacteria. DegU is a response regulator that controls several social behaviours exhibited by B. subtilis including swarming motility, biofilm formation and extracellular protease (exoprotease) production. Here, for the first time, we examine the prevalence and origin of exoprotease-producing cells within the biofilm. This was accomplished using single-cell analysis techniques including flow cytometry and fluorescence microscopy. We established that the number of exoprotease-producing cells increases as the biofilm matures. This is reflected by both an increase at the level of transcription and an increase in exoprotease activity over time. We go on to demonstrate that exoprotease-producing cells arise from more than one cell type, namely matrix-producing and non-matrix-producing cells. In toto these findings allow us to add exoprotease-producing cells to the list of specialized cell types that are derived during B. subtilis biofilm formation and furthermore the data highlight the plasticity in the origin of differentiated cells.

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