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 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 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 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.

Single-Cell Sequencing Technologies for Cardiac Stem Cell Studies

2017 ◽  
Vol 26 (21) ◽  
pp. 1540-1551 ◽  
Author(s):  
Tiantian Liu ◽  
Hongjin Wu ◽  
Shixiu Wu ◽  
Charles Wang
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Cardiac Stem Cell ◽  
Single Cell Sequencing ◽  
Sequencing Technologies

scholarly journals Discovering Cellular Mitochondrial Heteroplasmy Heterogeneity with Single Cell RNA and ATAC Sequencing

Biology ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 503
Author(s):  
Aidan S. Marshall ◽  
Nick S. Jones
Keyword(s):  
Next Generation Sequencing ◽  
Single Cell ◽  
Cellular Heterogeneity ◽  
Open Problems ◽  
Single Cell Sequencing ◽  
Sequencing Technologies ◽  
Single Cell Rna Sequencing ◽  
Mitochondrial Heteroplasmy ◽  
Mitochondrial Heterogeneity ◽  
Generation Sequencing

Next-generation sequencing technologies have revolutionised the study of biological systems by enabling the examination of a broad range of tissues. Its application to single-cell genomics has generated a dynamic and evolving field with a vast amount of research highlighting heterogeneity in transcriptional, genetic and epigenomic state between cells. However, compared to these aspects of cellular heterogeneity, relatively little has been gleaned from single-cell datasets regarding cellular mitochondrial heterogeneity. Single-cell sequencing techniques can provide coverage of the mitochondrial genome which allows researchers to probe heteroplasmies at the level of the single cell, and observe interactions with cellular function. In this review, we give an overview of two popular single-cell modalities—single-cell RNA sequencing and single-cell ATAC sequencing—whose throughput and widespread usage offers researchers the chance to probe heteroplasmy combined with cell state in detailed resolution across thousands of cells. After summarising these technologies in the context of mitochondrial research, we give an overview of recent methods which have used these approaches for discovering mitochondrial heterogeneity. We conclude by highlighting current limitations of these approaches and open problems for future consideration.

scholarly journals Uphyloplot2: visualizing phylogenetic trees from single-cell RNA-seq data

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Stefan Kurtenbach ◽  
Anthony M. Cruz ◽  
Daniel A. Rodriguez ◽  
Michael A. Durante ◽  
J. William Harbour
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Copy Number ◽  
Phylogenetic Trees ◽  
Copy Number Variations ◽  
Rna Seq ◽  
Single Cell Sequencing ◽  
Sequencing Technologies ◽  
Manual Analysis ◽  
Tumor Clonality

Abstract Background Recent advances in single cell sequencing technologies allow for greater resolution in assessing tumor clonality using chromosome copy number variations (CNVs). While single cell DNA sequencing technologies are ideal to identify tumor sub-clones, they remain expensive and in contrast to single cell RNA-seq (scRNA-seq) methods are more limited in the data they generate. However, CNV data can be inferred from scRNA-seq and bulk RNA-seq, for which several tools have been developed, including inferCNV, CaSpER, and HoneyBADGER. Inferences regarding tumor clonality from CNV data (and other sources) are frequently visualized using phylogenetic plots, which previously required time-consuming and error-prone, manual analysis. Results Here, we present Uphyloplot2, a python script that generates phylogenetic plots directly from inferred RNA-seq data, or any Newick formatted dendrogram file. The tool is publicly available at https://github.com/harbourlab/UPhyloplot2/. Conclusions Uphyloplot2 is an easy-to-use tool to generate phylogenetic plots to depict tumor clonality from scRNA-seq data and other sources.

scholarly journals Transitions in development – an interview with Tom Nowakowski

Development ◽  
2021 ◽  
Vol 148 (19) ◽  
Author(s):  
Alex Eve
Keyword(s):  
Single Cell ◽  
San Francisco ◽  
Assistant Professor ◽  
Group Leader ◽  
University Of California ◽  
Next Generation ◽  
Brain Science ◽  
Single Cell Sequencing ◽  
Sequencing Technologies ◽  
The Future

Tom Nowakowski is an Assistant Professor at University of California San Francisco (UCSF), where he uses single-cell sequencing technologies to study neurodevelopment. He is also a Chan Zuckerberg Biohub Investigator and a Next Generation Leader at the Allen Institute for Brain Science. We met with Tom over Zoom to hear more about his career, his transition to becoming a group leader and his plans for the future.

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