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      Dive into Single, Seek Out Multiple: Probing Cancer Metastases via Single-Cell Sequencing and Imaging Techniques

      review-article
      , *
      Cancers
      MDPI
      cancer metastasis, single-cell sequencing, single-cell imaging

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          Abstract

          Simple Summary

          Treating cancer metastasis is the biggest challenge in clinical practice. It is largely due to our limited understanding of the complex process, which involves not only the evolution and/or selection of heterogeneous primary tumor cells to metastatic tumors, but also interaction and/or adaption with various types of cells in different microenvironments, including temporally in the circulation system. These limitations are currently resolved by single-cell technologies. This review summarizes recent applications of single-cell technologies in metastatic studies, highlights the unique findings, and discusses the future directions.

          Abstract

          Metastasis is the cause of most cancer deaths and continues to be the biggest challenge in clinical practice and laboratory investigation. The challenge is largely due to the intrinsic heterogeneity of primary and metastatic tumor populations and the complex interactions among cancer cells and cells in the tumor microenvironment. Therefore, it is important to determine the genotype and phenotype of individual cells so that the metastasis-driving events can be precisely identified, understood, and targeted in future therapies. Single-cell sequencing techniques have allowed the direct comparison of the genomic and transcriptomic changes among different stages of metastatic samples. Single-cell imaging approaches have enabled the live visualization of the heterogeneous behaviors of malignant and non-malignant cells in the tumor microenvironment. By applying these technologies, we are achieving a spatiotemporal precision understanding of cancer metastases and clinical therapeutic translations.

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          Most cited references121

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype

            Rapid advances in next-generation sequencing technologies have dramatically changed our ability to perform genome-scale analyses. The human reference genome used for most genomic analyses represents only a small number of individuals, limiting its usefulness for genotyping. We designed a novel method, HISAT2, for representing and searching an expanded model of the human reference genome, in which a large catalogue of known genomic variants and haplotypes is incorporated into the data structure used for searching and alignment. This strategy for representing a population of genomes, along with a fast and memory-efficient search algorithm, enables more detailed and accurate variant analyses than previous methods. We demonstrate two initial applications of HISAT2: HLA typing, a critical need in human organ transplantation, and DNA fingerprinting, widely used in forensics. These applications are part of HISAT-genotype, with performance not only surpassing earlier computational methods, but matching or exceeding the accuracy of laboratory-based assays.
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              Near-optimal probabilistic RNA-seq quantification.

              We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Cancers (Basel)
                Cancers (Basel)
                cancers
                Cancers
                MDPI
                2072-6694
                03 March 2021
                March 2021
                : 13
                : 5
                : 1067
                Affiliations
                Department of Cancer Biology, College of Medicine and Life Sciences, The University of Toledo, Toledo, OH 43614, USA; shang.su@ 123456utoledo.edu
                Author notes
                Author information
                https://orcid.org/0000-0003-0228-5380
                https://orcid.org/0000-0001-5074-193X
                Article
                cancers-13-01067
                10.3390/cancers13051067
                7959126
                34944922
                86e39141-c8a7-445a-84c3-a0ce5a2a5c4c
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 18 January 2021
                : 27 February 2021
                Categories
                Review

                cancer metastasis,single-cell sequencing,single-cell imaging

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