scholarly journals XenoCell: classification of cellular barcodes in single cell experiments from xenograft samples

2019 ◽  
Author(s):  
Stefano Cheloni ◽  
Roman Hillje ◽  
Lucilla Luzi ◽  
Pier Giuseppe Pelicci ◽  
Elena Gatti
Keyword(s):  
Single Cell ◽  
Biological Systems ◽  
Single Cell Sequencing ◽  
Commercial Use ◽  
Link Type ◽  
Sequencing Technologies ◽  
Mouse Cells ◽  
Reliable Classification ◽  
Human And Mouse

AbstractSingle-cell sequencing technologies provide unprecedented opportunities to deconvolve the genomic, transcriptomic or epigenomic heterogeneity of complex biological systems. Its application in samples from xenografts of patient-derived biopsies (PDX), however, is limited by the presence in the analysed samples of a mixture of cells arising from the host and the graft.We have developed XenoCell, the first stand-alone pre-processing tool that performs fast and reliable classification of host and graft cellular barcodes. We show its application on a single cell dataset composed by human and mouse cells.Availability and implementationXenoCell is available for non-commercial use on GitLab: https://gitlab.com/XenoCell/XenoCell

scholarly journals XenoCell: classification of cellular barcodes in single cell experiments from xenograft samples

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Stefano Cheloni ◽  
Roman Hillje ◽  
Lucilla Luzi ◽  
Pier Giuseppe Pelicci ◽  
Elena Gatti
Keyword(s):  
Single Cell ◽  
Open Source ◽  
Cell Line ◽  
Mixed Species ◽  
Single Cell Sequencing ◽  
Sequencing Technologies ◽  
Cell Experiment ◽  
Reliable Classification ◽  
Bioinformatics Workflows

Abstract Background Single-cell sequencing technologies provide unprecedented opportunities to deconvolve the genomic, transcriptomic or epigenomic heterogeneity of complex biological systems. Its application in samples from xenografts of patient-derived biopsies (PDX), however, is limited by the presence of cells originating from both the host and the graft in the analysed samples; in fact, in the bioinformatics workflows it is still a challenge discriminating between host and graft sequence reads obtained in a single-cell experiment. Results We have developed XenoCell, the first stand-alone pre-processing tool that performs fast and reliable classification of host and graft cellular barcodes from single-cell sequencing experiments. We show its application on a mixed species 50:50 cell line experiment from 10× Genomics platform, and on a publicly available PDX dataset obtained by Drop-Seq. Conclusions XenoCell accurately dissects sequence reads from any host and graft combination of species as well as from a broad range of single-cell experiments and platforms. It is open source and available at https://gitlab.com/XenoCell/XenoCell.

Single cell sequencing: An engineer and business person's perspective on how we got here

Lab on a Chip ◽  
2017 ◽  
Vol 17 (20) ◽  
pp. 3349-3350
Author(s):  
Mark Gilligan
Keyword(s):  
Single Cell ◽  
Single Cell Sequencing ◽  
Sequencing Technologies

Microfluidics entrepreneur Mark Gilligan provides a perspective on the development of single-cell sequencing technologies.

scholarly journals SCOPIT: sample size calculations for single-cell sequencing experiments

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Alexander Davis ◽  
Ruli Gao ◽  
Nicholas E. Navin
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Multinomial Distribution ◽  
R Package ◽  
Cell Type ◽  
Single Cell Sequencing ◽  
Link Type ◽  
Dna And Rna ◽  
Sample Size Calculations ◽  
Number Of Cells

Abstract Background In single cell DNA and RNA sequencing experiments, the number of cells to sequence must be decided before running an experiment, and afterwards, it is necessary to decide whether sufficient cells were sampled. These questions can be addressed by calculating the probability of sampling at least a defined number of cells from each subpopulation (cell type or cancer clone). Results We developed an interactive web application called SCOPIT (Single-Cell One-sided Probability Interactive Tool), which calculates the required probabilities using a multinomial distribution (www.navinlab.com/SCOPIT). In addition, we created an R package called pmultinom for scripting these calculations. Conclusions Our tool for fast multinomial calculations provide a simple and intuitive procedure for prospectively planning single-cell experiments or retrospectively evaluating if sufficient numbers of cells have been sequenced. The web application can be accessed at navinlab.com/SCOPIT.

scholarly journals Deciphering Tumor Heterogeneity in Hepatocellular Carcinoma (HCC)—Multi-Omic and Singulomic Approaches

2021 ◽  
Author(s):  
Renumathy Dhanasekaran
Keyword(s):  
Single Cell ◽  
Tumor Heterogeneity ◽  
Immune Cell ◽  
Single Cell Analysis ◽  
Sampling Strategies ◽  
Cell Analysis ◽  
Single Cell Sequencing ◽  
Sequencing Technologies ◽  
Hepatocellular Carcinomas ◽  
Generation Sequencing

AbstractTumor heterogeneity, a key hallmark of hepatocellular carcinomas (HCCs), poses a significant challenge to developing effective therapies or predicting clinical outcomes in HCC. Recent advances in next-generation sequencing-based multi-omic and single cell analysis technologies have enabled us to develop high-resolution atlases of tumors and pull back the curtain on tumor heterogeneity. By combining multiregion targeting sampling strategies with deep sequencing of the genome, transcriptome, epigenome, and proteome, several studies have revealed novel mechanistic insights into tumor initiation and progression in HCC. Advances in multiparametric immune cell profiling have facilitated a deeper dive into the biological complexity of HCC, which is crucial in this era of immunotherapy. Moreover, studies using liquid biopsy have demonstrated their potential to circumvent the need for tissue sampling to investigate heterogeneity. In this review, we discuss how multi-omic and single-cell sequencing technologies have advanced our understanding of tumor heterogeneity in HCC.

scholarly journals Application of single-cell sequencing technologies in pancreatic cancer

2021 ◽  
Author(s):  
Mastan Mannarapu ◽  
Begum Dariya ◽  
Obul Reddy Bandapalli
Keyword(s):  
Pancreatic Cancer ◽  
Single Cell ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Single Cells ◽  
Evolutionary Relationship ◽  
Intratumor Heterogeneity ◽  
Sequencing Technology ◽  
Single Cell Sequencing ◽  
Sequencing Technologies

AbstractPancreatic cancer (PC) is the third lethal disease for cancer-related mortalities globally. This is mainly because of the aggressive nature and heterogeneity of the disease that is diagnosed only in their advanced stages. Thus, it is challenging for researchers and clinicians to study the molecular mechanism involved in the development of this aggressive disease. The single-cell sequencing technology enables researchers to study each and every individual cell in a single tumor. It can be used to detect genome, transcriptome, and multi-omics of single cells. The current single-cell sequencing technology is now becoming an important tool for the biological analysis of cells, to find evolutionary relationship between multiple cells and unmask the heterogeneity present in the tumor cells. Moreover, its sensitivity nature is found progressive enabling to detect rare cancer cells, circulating tumor cells, metastatic cells, and analyze the intratumor heterogeneity. Furthermore, these single-cell sequencing technologies also promoted personalized treatment strategies and next-generation sequencing to predict the disease. In this review, we have focused on the applications of single-cell sequencing technology in identifying cancer-associated cells like cancer-associated fibroblast via detecting circulating tumor cells. We also included advanced technologies involved in single-cell sequencing and their advantages. The future research indeed brings the single-cell sequencing into the clinical arena and thus could be beneficial for diagnosis and therapy of PC patients.

scholarly journals HD Spot: Interpretable Deep Learning Classification of Single Cell Transcript Data

2019 ◽  
Author(s):  
Eric Prince ◽  
Todd C. Hankinson
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
High Throughput ◽  
Ground Truth ◽  
Sequencing Technologies ◽  
Bioinformatic Tool ◽  
Complex Relationships ◽  
Insight Into ◽  
Generation Sequencing

ABSTRACTHigh throughput data is commonplace in biomedical research as seen with technologies such as single-cell RNA sequencing (scRNA-seq) and other Next Generation Sequencing technologies. As these techniques continue to be increasingly utilized it is critical to have analysis tools that can identify meaningful complex relationships between variables (i.e., in the case of scRNA-seq: genes) in a way such that human bias is absent. Moreover, it is equally paramount that both linear and non-linear (i.e., one-to-many) variable relationships be considered when contrasting datasets. HD Spot is a deep learning-based framework that generates an optimal interpretable classifier a given high-throughput dataset using a simple genetic algorithm as well as an autoencoder to classifier transfer learning approach. Using four unique publicly available scRNA-seq datasets with published ground truth, we demonstrate the robustness of HD Spot and the ability to identify ontologically accurate gene lists for a given data subset. HD Spot serves as a bioinformatic tool to allow novice and advanced analysts to gain complex insight into their respective datasets enabling novel hypotheses development.

scholarly journals BABEL enables cross-modality translation between multi-omic profiles at single-cell resolution

2020 ◽  
Author(s):  
Kevin E. Wu ◽  
Kathryn E. Yost ◽  
Howard Y. Chang ◽  
James Zou
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Exploration ◽  
Grand Challenge ◽  
Fine Grained ◽  
Powerful Approach ◽  
Technical Innovations ◽  
Cell Data ◽  
Human And Mouse

AbstractSimultaneous profiling of multi-omic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility – for example generating paired measurements of scRNA-seq and scATAC-seq – wide-spread application of joint profiling is challenging due to the experimental complexity, noise, and cost. Here we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging a novel interoperable neural network model, BABEL can generate scRNA-seq directly from a cell’s scATAC-seq, and vice versa. This makes it possible to computationally synthesize paired multi-omic measurements when only one modality is experimentally available. Across several paired scRNA-seq and scATAC-seq datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to new biological contexts not seen during training. For example, starting from scATAC-seq of patient derived basal cell carcinoma (BCC), BABEL generated scRNA-seq that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of the experimental BCC scRNA-seq data. We further show that BABEL can incorporate additional single-cell data modalities, such as CITE-seq, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation.

scholarly journals ACME dissociation: a versatile cell fixation-dissociation method for single-cell transcriptomics

2020 ◽  
Author(s):  
Helena García-Castro ◽  
Nathan J Kenny ◽  
Patricia Álvarez-Campos ◽  
Vincent Mason ◽  
Anna Schönauer ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Sorting ◽  
Cell Types ◽  
Cell Dissociation ◽  
Rna Integrity ◽  
Single Cell Sequencing ◽  
Sequencing Technologies ◽  
A Cell ◽  
Dissociated Cells ◽  
Cell Fixation

AbstractSingle-cell sequencing technologies are revolutionizing biology, but are limited by the need to dissociate fresh samples that can only be fixed at later stages. We present ACME (ACetic-MEthanol) dissociation, a cell dissociation approach that fixes cells as they are being dissociated. ACME-dissociated cells have high RNA integrity, can be cryopreserved multiple times, can be sorted by Fluorescence-Activated Cell Sorting (FACS) and are permeable, enabling combinatorial single-cell transcriptomic approaches. As a proof of principle, we have performed SPLiT-seq with ACME cells to obtain around ∼34K single cell transcriptomes from two planarian species and identified all previously described cell types in similar proportions. ACME is based on affordable reagents, can be done in most laboratories and even in the field, and thus will accelerate our knowledge of cell types across the tree of life.

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