scholarly journals Sparsely-Connected Autoencoder (SCA) for single cell RNAseq data mining

2020 ◽  
Author(s):  
L Alessandri ◽  
F Cordero ◽  
M Beccuti ◽  
N Licheri ◽  
M Arigoni ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Medical Personnel ◽  
Cellular Heterogeneity ◽  
Biological Information ◽  
The Neural Network ◽  
Rnaseq Data ◽  
Functional Features ◽  
Cell Subpopulations

AbstractSingle-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Although scRNAseq has some technical challenges, it would be of great interest being able to disclose biological information out of cell subpopulations, which can be defined by cluster analysis of scRNAseq data. In this manuscript, we evaluated the efficacy of sparsely-connected autoencoder (SCA) as tool for the functional mining of single cells clusters. We show that SCA can be uses as tool to uncover hidden features associated to scRNAseq data. Our approach is strengthened by two metrics, QCF and QCM, which respectively allow to evaluate the ability of SCA to reconstruct a cells cluster and to evaluate the overall quality of the neural network model. Our data indicate that SCA encoded spaces, derived by different experimentally validated data (TFs targets, miRNAs targets, Kinases targets, and cancer-related immune signatures), can be used to grasp single cell cluster-specific functional features. In our implementation, SCA efficacy comes from its ability to reconstruct only specific clusters, thus indicating only those clusters where the SCA encoding space is a key element for cells aggregation. SCA analysis is implemented as module in rCASC framework and it is supported by a GUI to simplify it usage for biologists and medical personnel.

scholarly journals Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Luca Alessandri ◽  
Francesca Cordero ◽  
Marco Beccuti ◽  
Nicola Licheri ◽  
Maddalena Arigoni ◽  
...  
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Medical Personnel ◽  
Cellular Heterogeneity ◽  
Biological Information ◽  
Cell Clusters ◽  
The Neural Network ◽  
Rnaseq Data ◽  
Functional Features ◽  
Cell Subpopulations

AbstractSingle-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Thus, it would be of great interest being able to disclose biological information belonging to cell subpopulations, which can be defined by clustering analysis of scRNAseq data. In this manuscript, we report a tool that we developed for the functional mining of single cell clusters based on Sparsely-Connected Autoencoder (SCA). This tool allows uncovering hidden features associated with scRNAseq data. We implemented two new metrics, QCC (Quality Control of Cluster) and QCM (Quality Control of Model), which allow quantifying the ability of SCA to reconstruct valuable cell clusters and to evaluate the quality of the neural network achievements, respectively. Our data indicate that SCA encoded space, derived by different experimentally validated data (TF targets, miRNA targets, Kinase targets, and cancer-related immune signatures), can be used to grasp single cell cluster-specific functional features. In our implementation, SCA efficacy comes from its ability to reconstruct only specific clusters, thus indicating only those clusters where the SCA encoding space is a key element for cells aggregation. SCA analysis is implemented as module in rCASC framework and it is supported by a GUI to simplify it usage for biologists and medical personnel.

scholarly journals Comprehensive benchmarking of single cell RNA sequencing technologies for characterizing cellular perturbation

2020 ◽  
Author(s):  
Verboom Karen ◽  
Alemu T Assefa ◽  
Nurten Yigit ◽  
Jasper Anckaert ◽  
Niels Vandamme ◽  
...  
Keyword(s):  
Single Cell ◽  
Differentially Expressed Genes ◽  
Single Cells ◽  
Neuroblastoma Cells ◽  
Transcriptional Response ◽  
Differentially Expressed ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Library Size ◽  
Cell Subpopulations

ABSTRACTTechnological advances in transcriptome sequencing of single cells continues to provide an unprecedented view on tissue composition and cellular heterogeneity. While several studies have compared different single cell RNA-seq methods with respect to data quality and their ability to distinguish cell subpopulations, none of these studies investigated the heterogeneity of the cellular transcriptional response upon a chemical perturbation. In this study, we evaluated the transcriptional response of NGP neuroblastoma cells upon nutlin-3 treatment using the C1, ddSeq and Chromium single cell systems. These devices and library preparation methods are representative for the wide variety of platforms, ranging from microfluid chips to droplet-based systems and from full transcript sequencing to 3-prime end sequencing. In parallel, we used bulk RNA-seq for molecular characterization of the transcriptional response. Two complementary metrics to evaluate performance were applied: the first is the number and identity of differentially expressed genes as defined in consensus by two statistical models, and the second is the enrichment analysis of biological signals. Where relevant, to make the data more comparable, we downsampled sequencing library size, selected cell subpopulations based on specific RNA abundance features, or created pseudobulk samples. While the C1 detects the highest number of genes per cell and better resembles bulk RNA-seq, the Chromium identifies most differentially expressed genes, albeit still substantially fewer than bulk RNA-seq. Gene set enrichment analyses reveals that detection of a limited set of the most abundant genes in single cell RNA-seq experiments is sufficient for molecular phenotyping. Finally, single cell RNA-seq reveals a heterogeneous response of NGP neuroblastoma cells upon nutlin-3 treatment, revealing putative late-responder or resistant cells, both undetected in bulk RNA-seq experiments.

scholarly journals CoTECH for single-cell joint detection of transcriptome and chromatin occupancy

2021 ◽  
Author(s):  
Yingjie Luo ◽  
Haiqing Xiong ◽  
Qianhao Wang ◽  
Xianhong Yu ◽  
Aibin He
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
High Throughput ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Joint Detection ◽  
Biological Processes ◽  
Experimental Procedure ◽  
Higher Dimensional

Abstract Here we present CoTECH, a high-throughput co-aasay that measures chromatin occupancy and transcriptome in single cells. The CoTECH method adopts a combinatorial indexing strategy to enrich chromatin fragments of interest as reported in CoBATCH in combination with a modified Smart-seq2 procedure to simultaneously capture the 3’ mRNA profiles in the same single cells. The whole experimental procedure can be handled within three days.The CoTECH acquires data quality of 1000-9000 unique mapped reads (DNA partition) and 1500-4000 expressed genes (RNA partition) per cell. Experimentally linking chromatin occupancy to transcriptional outputs and inferred molecular association between multimodal omics datasets made possible by CoTECH enables reconstructions of higher dimensional epigenomic landscape, providing new insights into epigenome-centric gene regulation and cellular heterogeneity in many biological processes. This step-by-step protocol is related to the publication “Single-cell joint detection of chromatin occupancy and transcriptome enables higher-dimensional epigenomic reconstructions” in Nature Methods.

scholarly journals Cluster similarity spectrum integration of single-cell genomics data

2020 ◽  
Author(s):  
Zhisong He ◽  
Agnieska Brazovskaja ◽  
Sebastian Ebert ◽  
J. Gray Camp ◽  
Barbara Treutlein
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Data Representation ◽  
Cellular Heterogeneity ◽  
Biological Information ◽  
Sequencing Data ◽  
Experimental Conditions ◽  
Complex Biological System ◽  
Free Data ◽  
Powerful Approach

Technologies to sequence the transcriptome, genome or epigenome from thousands of single cells in an experiment provide extraordinary resolution into the molecular states present within a complex biological system at any given moment. However, it is a major challenge to integrate single-cell sequencing data across experiments, conditions, batches, timepoints and other technical considerations. New computational methods are required that can integrate samples while simultaneously preserving biological information. Here, we propose an unsupervised reference-free data representation, Cluster Similarity Spectrum (CSS), where each cell is represented by its similarities to clusters independently identified across samples. We show that CSS can be used to assess cellular heterogeneity and enable reconstruction of differentiation trajectories from cerebral organoid single-cell transcriptomic data, and to integrate data across experimental conditions and human individuals. We compare CSS to other integration algorithms and show that it can outperform other methods in certain integration scenarios. We also show that CSS allows projection of single-cell genomic data of different modalities to the CSS-represented reference atlas for visualization and cell type identity prediction. In summary, CSS provides a straightforward and powerful approach to understand and integrate challenging single-cell multi-omic data.

scholarly journals Dynamic Analysis of Alternative Polyadenylation from Single-Cell RNA-Seq (scDaPars) Reveals Cell Subpopulations Invisible to Gene Expression Analysis

2020 ◽  
Author(s):  
Yipeng Gao ◽  
Lei Li ◽  
Christopher I. Amos ◽  
Wei Li
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Single Cells ◽  
Single Gene ◽  
Simulated Data ◽  
Alternative Polyadenylation ◽  
Cellular Heterogeneity ◽  
Cell Subpopulations

AbstractAlternative polyadenylation (APA) is a major mechanism of post-transcriptional regulation in various cellular processes including cell proliferation and differentiation, but the APA heterogeneity among single cells remains largely unknown. Single-cell RNA sequencing (scRNA-seq) has been extensively used to define cell subpopulations at the transcription level. Yet, most scRNA-seq data have not been analyzed in an “APA-aware” manner. Here, we introduce scDaPars, a bioinformatics algorithm to accurately quantify APA events at both single-cell and single-gene resolution using standard scRNA-seq data. Validations in both real and simulated data indicate that scDaPars can robustly recover missing APA events caused by the low amounts of mRNA sequenced in single cells. When applied to cancer and human endoderm differentiation data, scDaPars not only revealed cell-type-specific APA regulation but also identified cell subpopulations that are otherwise invisible to conventional gene expression analysis. Thus, scDaPars will enable us to understand cellular heterogeneity at the post-transcriptional APA level.

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 EPEN-31. SINGLE-CELL RNAseq OF CHILDHOOD EPENDYMOMA REVEALS DISTINCT NEOPLASTIC CELL SUBPOPULATIONS THAT IMPACT ETIOLOGY, MOLECULAR CLASSIFICATION AND OUTCOME

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii314-iii314
Author(s):  
Andrew Donson ◽  
Austin Gillen ◽  
Riemondy Kent ◽  
Ahmed Gilani ◽  
Sujatha Venkataraman ◽  
...  
Keyword(s):  
Single Cell ◽  
Histological Analysis ◽  
Molecular Classification ◽  
Neoplastic Cell ◽  
Good Prognosis ◽  
Cellular Heterogeneity ◽  
Mesenchymal Phenotype ◽  
Ependymal Differentiation ◽  
Cell Subpopulations ◽  
Cell Niche

Abstract Ependymoma (EPN) is a brain tumor commonly presenting in childhood that remains fatal in the majority of children. Intra-tumoral cellular heterogeneity in bulk-tumor samples significantly confounds our understanding of EPN biology, impeding development of effective therapy. We therefore used single-cell RNA sequencing to catalog cellular heterogeneity of 26 childhood EPN, predominantly from ST-RELA, PFA1 and PFA2 subgroups. ST-RELA and PFA subgroups clustered separately, with ST-RELA clustering largely according to individual sample-of-origin. PFA1 and PFA2 subgroup EPNs cells were intermixed and revealed 4 major subpopulations – 2 with characteristics of ependymal differentiation (transporter and ciliated phenotype subpopulations), an undifferentiated subpopulation and a mesenchymal phenotype. Pseudotime analysis showed the undifferentiated progenitor subpopulation either differentiating into ependymal differentiation subpopulations or transitioning into the mesenchymal subpopulation. Histological analysis revealed that undifferentiated and mesenchymal subpopulations cells colocalized to perinecrotic/perivascular zones, the putative ependymoma stem cell niche. Deconvolution of PFA bulk transcriptome data showed that undifferentiated and mesenchymal subpopulations were associated with a poor prognosis; whereas the ciliated ependymal cell-differentiated subpopulation was associated with a good prognosis. In conflict with current distinct classification paradigms, the ratio of mesenchymal and ciliated subpopulations determined bulk-tumor subgroups assignment to PFA1 and PFA2 respectively. This atlas of EPN cellular heterogeneity provides an important advance in our understanding of EPN biology, identifying high-risk associated subpopulations for therapeutic targeting.

scholarly journals The Development of Single-Cell Metabolism and Its Role in Studying Cancer Emergent Properties

2022 ◽  
Vol 11 ◽  
Author(s):  
Dingju Wei ◽  
Meng Xu ◽  
Zhihua Wang ◽  
Jingjing Tong
Keyword(s):  
Tumor Cell ◽  
Single Cell ◽  
Immune Escape ◽  
Cell Metabolism ◽  
Single Cells ◽  
Metabolic Reprogramming ◽  
Biological Information ◽  
Emergent Properties ◽  
Anti Cancer ◽  
Cell Metabolomics

Metabolic reprogramming is one of the hallmarks of malignant tumors, which provides energy and material basis for tumor rapid proliferation, immune escape, as well as extensive invasion and metastasis. Blocking the energy and material supply of tumor cells is one of the strategies to treat tumor, however tumor cell metabolic heterogeneity prevents metabolic-based anti-cancer treatment. Therefore, searching for the key metabolic factors that regulate cell cancerous change and tumor recurrence has become a major challenge. Emerging technology––single-cell metabolomics is different from the traditional metabolomics that obtains average information of a group of cells. Single-cell metabolomics identifies the metabolites of single cells in different states by mass spectrometry, and captures the molecular biological information of the energy and substances synthesized in single cells, which provides more detailed information for tumor treatment metabolic target screening. This review will combine the current research status of tumor cell metabolism with the advantages of single-cell metabolomics technology, and explore the role of single-cell sequencing technology in searching key factors regulating tumor metabolism. The addition of single-cell technology will accelerate the development of metabolism-based anti-cancer strategies, which may greatly improve the prognostic survival rate of cancer patients.

scholarly journals Assessing the measurement transfer function of single-cell RNA sequencing

2016 ◽  
Author(s):  
Hannah R. Dueck ◽  
Rizi Ai ◽  
Adrian Camarena ◽  
Bo Ding ◽  
Reymundo Dominguez ◽  
...  
Keyword(s):  
Transfer Function ◽  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Transfer Functions ◽  
Single Cells ◽  
Biological Information ◽  
Gene Detection ◽  
Single Cell Rna Sequencing ◽  
Scale Control

AbstractRecently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known. Here, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate the measurement transfer functions to be linear above ~5-10 molecules. Based on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.

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.

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