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.

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

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 Single-cell sequencing of genomic DNA resolves sub-clonal heterogeneity in a melanoma cell line

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Enrique I. Velazquez-Villarreal ◽  
Shamoni Maheshwari ◽  
Jon Sorenson ◽  
Ian T. Fiddes ◽  
Vijay Kumar ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Melanoma Cell ◽  
Genomic Dna ◽  
Melanoma Cell Line ◽  
Clonal Heterogeneity ◽  
Single Cell Sequencing

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 Resolving sub-clonal heterogeneity within cell-line growths by single cell sequencing genomic DNA

2019 ◽  
Author(s):  
Enrique I. Velazquez-Villarreal ◽  
Shamoni Maheshwari ◽  
Jon Sorenson ◽  
Ian T. Fiddes ◽  
Vijay Kumar ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Copy Number ◽  
Genomic Dna ◽  
Break Point ◽  
Chromosome 8 ◽  
Sequencing Data ◽  
Melanoma Tumor ◽  
Aggregated Data ◽  
Single Cell Sequencing

ABSTRACTWe performed shallow single-cell sequencing of genomic DNA across 1,475 cells from a well-studied cell-line, COLO829, to resolve overall tumor complexity and clonality. This melanoma tumor-line has been previously characterized by multiple technologies and provides a benchmark for evaluating somatic alterations, though has exhibited conflicting and indeterminate copy number states. We identified at least four major sub-clones by discriminant analysis of principal components (DAPC) of single cell copy number data. Break-point and loss of heterozygosity (LOH) analysis of aggregated data from sub-clones revealed a complex rearrangement of chromosomes 1, 10 and 18 that was maintained in all but two sub-clones. Likewise, two of the sub-clones were distinguished by loss of 1 copy of chromosome 8. Re-analysis of previous spectral karyotyping data and bulk sequencing data recapitulated these sub-clone hallmark features and explains why the original bulk sequencing experiments generated conflicting copy number results. Overall, our results demonstrate how shallow copy number profiling together with clustering analysis of single cell sequencing can uncover significant hidden insights even in well studied cell-lines.

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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