Computational enhancement of single-cell sequences for inferring tumor evolution

AbstractMotivation: Tumor sequencing has entered an exciting phase with the advent of single-cell techniques that are revolutionizing the assessment of single nucleotide variation (SNV) at the highest cellular resolution. However, state-of-the-art single-cell sequencing technologies produce data with many missing bases (MBs) and incorrect base designations that lead to false-positive (FP) and false-negative (FN) detection of somatic mutations. While computational methods are available to make biological inferences in the presence of these errors, the accuracy of the imputed MBs and corrected FPs and FNs remains unknown.Results: Using computer simulated datasets, we assessed the robustness performance of four existing methods (OncoNEM, SCG, SCITE, and SiFit) and one new method (BEAM). BEAM is a Bayesian evolution-aware method that improves the quality of single-cell sequences by using the intrinsic evolutionary information in the single-cell data in a molecular phylogenetic framework. Overall, BEAM and SCITE performed the best. Most of the methods imputed MBs with high accuracy, but effective detection and correction of FPs and FNs require sampling a large number of SNVs. Analysis of an empirical dataset shows that computational methods can improve both the quality of tumor single-cell sequences and their utility for biological inference.Conclusions: Tumor cells descend from pre-existing cells, which creates evolutionary continuity in single-cell sequencing datasets. This information enables BEAM and other methods to correctly impute missing data and incorrect base assignments, but correction of FPs and FNs remains challenging when the number of SNVs sampled is small relative to the number of cells sequenced.Availability: BEAM is available on the web at https://github.com/SayakaMiura/BEAM.Contact:[email protected]

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

Lab on a Chip ◽

10.1039/c7lc90095c ◽

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.

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Effective clustering for single cell sequencing cancer data

10.1101/586545 ◽

2019 ◽

Cited By ~ 2

Author(s):

Simone Ciccolella ◽

Murray Patterson ◽

Paola Bonizzoni ◽

Gianluca Della Vedova

Keyword(s):

Single Cell ◽

Categorical Data ◽

Euclidean Distance ◽

Missing Values ◽

False Negative ◽

Ground Truth ◽

Sequencing Data ◽

Large Space ◽

Cancer Data ◽

Single Cell Sequencing

AbstractBackgroundSingle cell sequencing (SCS) technologies provide a level of resolution that makes it indispensable for inferring from a sequenced tumor, evolutionary trees or phylogenies representing an accumulation of cancerous mutations. A drawback of SCS is elevated false negative and missing value rates, resulting in a large space of possible solutions, which in turn makes infeasible using some approaches and tools. While this has not inhibited the development of methods for inferring phylogenies from SCS data, the continuing increase in size and resolution of these data begin to put a strain on such methods.One possible solution is to reduce the size of an SCS instance — usually represented as a matrix of presence, absence and missing values of the mutations found in the different sequenced cells — and infer the tree from this reduced-size instance. Previous approaches have used k-means to this end, clustering groups of mutations and/or cells, and using these means as the reduced instance. Such an approach typically uses the Euclidean distance for computing means. However, since the values in these matrices are of a categorical nature (having the three categories: present, absent and missing), we explore techniques for clustering categorical data — commonly used in data mining and machine learning — to SCS data, with this goal in mind.ResultsIn this work, we present a new clustering procedure aimed at clustering categorical vector, or matrix data — here representing SCS instances, called celluloid. We demonstrate that celluloid clusters mutations with high precision: never pairing too many mutations that are unrelated in the ground truth, but also obtains accurate results in terms of the phylogeny inferred downstream from the reduced instance produced by this method.Finally, we demonstrate the usefulness of a clustering step by applying the entire pipeline (clustering + inference method) to a real dataset, showing a significant reduction in the runtime, raising considerably the upper bound on the size of SCS instances which can be solved in practice.AvailabilityOur approach, celluloid: clustering single cell sequencing data around centroids is available at https://github.com/AlgoLab/celluloid/ under an MIT license.

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Application of Single-cell Sequencing Technologies in Reproductive Medicine

Reproductive and Developmental Medicine ◽

10.4103/wkmp-0152.210691 ◽

2017 ◽

Vol 1 (1) ◽

pp. 30

Author(s):

Jie Qiao ◽

Peng Yuan ◽

Li-Ying Yan

Keyword(s):

Single Cell ◽

Reproductive Medicine ◽

Single Cell Sequencing ◽

Sequencing Technologies

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A statistical test on single-cell data reveals widespread recurrent mutations in tumor evolution

10.1101/094722 ◽

2016 ◽

Cited By ~ 3

Author(s):

Jack Kuipers ◽

Katharina Jahn ◽

Benjamin J. Raphael ◽

Niko Beerenwinkel

Keyword(s):

Single Cell ◽

Large Scale ◽

Tumor Evolution ◽

Sequencing Data ◽

General Validity ◽

Genomic Deletions ◽

Single Cell Sequencing ◽

Statistical Framework ◽

Recurrent Mutations ◽

Complex Models

The infinite sites assumption, which states that every genomic position mutates at most once over the lifetime of a tumor, is central to current approaches for reconstructing mutation histories of tumors, but has never been tested explicitly. We developed a rigorous statistical framework to test the assumption with single-cell sequencing data. The framework accounts for the high noise and contamination present in such data. We found strong evidence for recurrent mutations at the same site in 8 out of 9 single-cell sequencing datasets from human tumors. Six cases involved the loss of earlier mutations, five of which occurred at sites unaffected by large scale genomic deletions. Two cases exhibited parallel mutation, including the dataset with the strongest evidence of recurrence. Our results refute the general validity of the infinite sites assumption and indicate that more complex models are needed to adequately quantify intra-tumor heterogeneity.

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Single‐cell sequencing and tumorigenesis: improved understanding of tumor evolution and metastasis

Clinical and Translational Medicine ◽

10.1186/s40169-017-0145-6 ◽

2017 ◽

Vol 6 (1) ◽

Cited By ~ 35

Author(s):

Darrell L. Ellsworth ◽

Heather L. Blackburn ◽

Craig D. Shriver ◽

Shahrooz Rabizadeh ◽

Patrick Soon‐Shiong ◽

...

Keyword(s):

Single Cell ◽

Tumor Evolution ◽

Single Cell Sequencing

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

Seminars in Liver Disease ◽

10.1055/s-0040-1722261 ◽

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.

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Application of single-cell sequencing technologies in pancreatic cancer

Molecular and Cellular Biochemistry ◽

10.1007/s11010-021-04095-4 ◽

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.

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

BMC Medical Genomics ◽

10.1186/s12920-021-00872-8 ◽

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.

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