New library construction method for single-cell genomes

Single-cell genome and transcriptome sequencing without upfront whole-genome amplification reveals cell state plasticity of melanoma subclones

Single-cell multi-omics methods enable the study of cell state diversity, which is largely determined by the interplay of the genome, epigenome, and transcriptome. Here, we describe Gtag&T-seq, a genome-and-transcriptome sequencing (G&T-seq) protocol of the same single cells that omits whole-genome amplification (WGA) by using direct genomic tagmentation (Gtag). Gtag drastically decreases the cost and improves coverage uniformity at single-cell and pseudo-bulk levels compared to WGA-based G&T-seq. We also show that transcriptome-based DNA copy number inference has limited resolution and accuracy, underlining the importance of affordable multi-omic approaches. Applying Gtag&T-seq to a melanoma xenograft model before treatment and at minimal residual disease revealed differential cell state plasticity and treatment response between cancer subclones. In summary, Gtag&T-seq is a low-cost and accurate single-cell multi-omics method that explores genetic alterations and their functional consequences in single cells at scale.

Hema-seq reveals genomic aberrations in a rare simultaneous occurrence of hematological malignancies

Co-occurrence of multiple myeloma and acute myelogenous leukemia is rare, with both malignancies often tracing back to multipotent hematopoietic stem cells. Cytogenetic techniques are the established baseline for diagnosis and characterization of complex hematological malignancies. In this study, we develop a workflow called Hema-seq to delineate clonal changes across various hematopoietic lineages through the integration of whole-genome sequencing, copy-number variations, cell morphology, and cytogenetic aberrations. In Hema-seq, cells are selected from Wright-stained slides and fluorescent probe-stained slides for sequencing. This technique therefore enables direct linking of whole-genome sequences to cytogenetic profiles. Through this method, we mapped sequential clonal alterations within the hematopoietic lineage, identifying critical shifts leading to myeloma and acute myeloid leukemia (AML) cell formations. By synthesizing data from each cell lineage, we provided insights into the hematopoietic tree's clonal evolution. Overall, this study highlights Hema-seq's capability in deciphering genomic heterogeneity in complex hematological malignancies, which can enable better diagnosis and treatment strategies.

Somatic variant calling from single-cell DNA sequencing data

Single-cell sequencing has gained popularity in recent years. Despite its numerous applications, single-cell DNA sequencing data is highly error-prone due to technical biases arising from uneven sequencing coverage, allelic dropout, and amplification error. With these artifacts, the identification of somatic genomic variants becomes a challenging task, and over the years, several methods have been developed explicitly for this type of data. Single-cell variant callers implement distinct strategies, make different use of the data, and typically result in many discordant calls when applied to real data. Here, we review current approaches for single-cell variant calling, emphasizing single nucleotide variants. We highlight their potential benefits and shortcomings to help users choose a suitable tool for their data at hand.

SCSilicon: a tool for synthetic single-cell DNA sequencing data generation

Background

Single-cell DNA sequencing is getting indispensable in the study of cell-specific cancer genomics. The performance of computational tools that tackle single-cell genome aberrations may be nevertheless undervalued or overvalued, owing to the insufficient size of benchmarking data. In silicon simulation is a cost-effective approach to generate as many single-cell genomes as possible in a controlled manner to make reliable and valid benchmarking.

Results

This study proposes a new tool, SCSilicon, which efficiently generates single-cell in silicon DNA reads with minimum manual intervention. SCSilicon automatically creates a set of genomic aberrations, including SNP, SNV, Indel, and CNV. Besides, SCSilicon yields the ground truth of CNV segmentation breakpoints and subclone cell labels. We have manually inspected a series of synthetic variations. We conducted a sanity check of the start-of-the-art single-cell CNV callers and found SCYN was the most robust one.

Conclusions

SCSilicon is a user-friendly software package for users to develop and benchmark single-cell CNV callers. Source code of SCSilicon is available at https://github.com/xikanfeng2/SCSilicon.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08566-w).

Sensitive and Low-Bias Transcriptome Sequencing Using Agarose PCR

Transcriptome sequencing has emerged as an important research tool for exploring the mysteries of life at the single-cell level. However, its wide application is limited by the bias associated with the amplification reactions which is essential for library building of trace RNA. In this study, low-melting-point agarose was added to the amplification reactions to take advantage of its molecular crowding effect and polymer cross-linked structure to improve the sensitivity of the reactions and reduce bias. To further evaluate the performance of the method, it was applied to transcriptome sequencing of microregion samples from brain tissue sections of mice with Parkinson's disease at the single cell level. The results showed that agarose PCR had better performance than in-tube PCR. Further application of agarose PCR to transcriptome library sequencing could obtain data closer to that of unamplified. With the addition of low melting point agarose, the sensitivity of the amplification reaction was significantly increased, while homogeneity was increased by approximately 2-fold. Not only that, but this work also provides 11% sensitivity improvement for spatial transcriptomic study on Parkinson's disease-associated gene detection. The agarose PCR provides a new tool for efficient and homogeneous amplification of trace samples and can be widely used for spatial transcriptome library sequencing and studies.

Reconstructing and counting genomic fragments through tagmentation-based haploid phasing

Single-cell sequencing provides a new level of granularity in studying the heterogeneous nature of cancer cells. For some cancers, this heterogeneity is the result of copy number changes of genes within the cellular genomes. The ability to accurately determine such copy number changes is critical in tracing and understanding tumorigenesis. Current single-cell genome sequencing methodologies infer copy numbers based on statistical approaches followed by rounding decimal numbers to integer values. Such methodologies are sample dependent, have varying calling sensitivities which heavily depend on the sample’s ploidy and are sensitive to noise in sequencing data. In this paper we have demonstrated the concept of integer-counting by using a novel bioinformatic algorithm built on our library construction chemistry in order to detect the discrete nature of the genome.

scDPN for High-throughput Single-cell CNV Detection to Uncover Clonal Evolution During HCC Recurrence

Single-cell genomics provides substantial resources for dissecting cellular heterogeneity and cancer evolution. Unfortunately, classical DNA amplification-based methods have low throughput and introduce coverage bias during sample preamplification. We developed a single-cell DNA library preparation method without preamplification in nanolitre scale (scDPN) to address these issues. The method achieved a throughput of up to 1800 cells per run for copy number variation (CNV) detection. Also, our approach demonstrated a lower level of amplification bias and noise than the multiple displacement amplification (MDA) method and showed high sensitivity and accuracy for cell line and tumor tissue evaluation. We used this approach to profile the tumor clones in paired primary and relapsed tumor samples of hepatocellular carcinoma (HCC). We identified three clonal subpopulations with a multitude of aneuploid alterations across the genome. Furthermore, we observed that a minor clone of the primary tumor containing additional alterations in chromosomes 1q, 10q, and 14q developed into the dominant clone in the recurrent tumor, indicating clonal selection during recurrence in HCC. Overall, this approach provides a comprehensive and scalable solution to understand genome heterogeneity and evolution

Breast tumours maintain a reservoir of subclonal diversity during expansion

Our knowledge of copy number evolution during the expansion of primary breast tumours is limited^1,2. Here, to investigate this process, we developed a single-cell, single-molecule DNA-sequencing method and performed copy number analysis of 16,178 single cells from 8 human triple-negative breast cancers and 4 cell lines. The results show that breast tumours and cell lines comprise a large milieu of subclones (7-22) that are organized into a few (3-5) major superclones. Evolutionary analysis suggests that after clonal TP53 mutations, multiple loss-of-heterozygosity events and genome doubling, there was a period of transient genomic instability followed by ongoing copy number evolution during the primary tumour expansion. By subcloning single daughter cells in culture, we show that tumour cells rediversify their genomes and do not retain isogenic properties. These data show that triple-negative breast cancers continue to evolve chromosome aberrations and maintain a reservoir of subclonal diversity during primary tumour growth.

A Highly Scalable Method for Joint Whole-Genome Sequencing and Gene-Expression Profiling of Single Cells

To address how genetic variation alters gene expression in complex cell mixtures, we developed direct nuclear tagmentation and RNA sequencing (DNTR-seq), which enables whole-genome and mRNA sequencing jointly in single cells. DNTR-seq readily identified minor subclones within leukemia patients. In a large-scale DNA damage screen, DNTR-seq was used to detect regions under purifying selection and identified genes where mRNA abundance was resistant to copy-number alteration, suggesting strong genetic compensation. mRNA sequencing (mRNA-seq) quality equals RNA-only methods, and the low positional bias of genomic libraries allowed detection of sub-megabase aberrations at ultra-low coverage. Each cell library is individually addressable and can be re-sequenced at increased depth, allowing multi-tiered study designs. Additionally, the direct tagmentation protocol enables coverage-independent estimation of ploidy, which can be used to identify cell singlets. Thus, DNTR-seq directly links each cell's state to its corresponding genome at scale, enabling routine analysis of heterogeneous tumors and other complex tissues.

Methods for copy number aberration detection from single-cell DNA-sequencing data

Copy number aberrations (CNAs), which are pathogenic copy number variations (CNVs), play an important role in the initiation and progression of cancer. Single-cell DNA-sequencing (scDNAseq) technologies produce data that is ideal for inferring CNAs. In this review, we review eight methods that have been developed for detecting CNAs in scDNAseq data, and categorize them according to the steps of a seven-step pipeline that they employ. Furthermore, we review models and methods for evolutionary analyses of CNAs from scDNAseq data and highlight advances and future research directions for computational methods for CNA detection from scDNAseq data.

Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data

Single-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs) have been implicated in the development and progression of various types of cancer. A wide array of methods for CNA detection has been either developed specifically for or adapted to single-cell DNA sequencing data. Understanding the strengths and limitations that are unique to each of these methods is very important for obtaining accurate copy number profiles from single-cell DNA sequencing data. We benchmarked three widely used methods–Ginkgo, HMMcopy, and CopyNumber–on simulated as well as real datasets. To facilitate this, we developed a novel simulator of single-cell genome evolution in the presence of CNAs. Furthermore, to assess performance on empirical data where the ground truth is unknown, we introduce a phylogeny-based measure for identifying potentially erroneous inferences. While single-cell DNA sequencing is very promising for elucidating and understanding CNAs, our findings show that even the best existing method does not exceed 80% accuracy. New methods that significantly improve upon the accuracy of these three methods are needed. Furthermore, with the large datasets being generated, the methods must be computationally efficient.

Copy number aberrations, or CNAs, refer to evolutionary events that act on cancer genomes by deleting segments of the genomes or introducing new copies of existing segments. These events have been implicated in various types of cancer; consequently, their accurate detection could shed light on the initiation and progression of tumor, as well as on the development of potential targeted therapeutics. Single-cell DNA sequencing technologies are now producing the type of data that would allow such detection at the resolution of individual cells. However, to achieve this detection task, methods have to implement several steps of “data wrangling” and dealing with technical artifacts. In this work, we benchmarked three widely used methods for CNA detection from single-cell DNA data, namely Ginkgo, HMMcopy, and CopyNumber. To accomplish this study, we developed a novel simulator and devised a phylogeny-based measure of potentially erroneous CNA calls. We find that none of these methods has high accuracy, and all of them can be computationally very demanding. These findings call for the development of more accurate and more efficient methods for CNA detection from single-cell DNA data.

The power of microRNAs as diagnostic and prognostic biomarkers in liquid biopsies

In the last decades, progresses in medical oncology have ameliorated the treatment of patients and their outcome. However, further improvements are still necessary, in particular for certain types of tumors such as pancreatic, gastric, and lung cancer as well as acute myeloid leukemia where early detection and monitoring of the disease are crucial for final patient outcome. Liquid biopsy represents a great advance in the field because it is less invasive, less time-consuming, and safer compared to classical biopsies and it can be useful to monitor the evolution of the disease as well as the response of patients to therapy. Liquid biopsy allows the detection of circulating tumor cells, nucleic acids, and exosomes not only in blood but also in different biological fluids: urine, saliva, pleural effusions, cerebrospinal fluid, and stool. Among the potential biomarkers detectable in liquid biopsies, microRNAs (miRNAs) are gaining more and more attention, since they are easily detectable, quite stable in biological fluids, and show high sensitivity. Many data demonstrate that miRNAs alone or in combination with other biomarkers could improve the diagnostic and prognostic power for many different tumors. Despite this, standardization of methods, sample preparation, and analysis remain challenging and a huge effort should be made to address these issues before miRNA biomarkers can enter the clinic. This review summarizes the main findings in the field of circulating miRNAs in both solid and hematological tumors.

Eleven grand challenges in single-cell data science

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Preparing Single-cell DNA Library Using Nextera for Detection of CNV

Single-cell DNA sequencing is a powerful tool to evaluate the state of heterogeneity of heterogeneous tissues like cancer in a quantitative manner that bulk sequencing can never achieve. DOP-PCR (Degenerate Oligonucleotide-Primed Polymerase Chain Reaction), MDA (Multiple Displacement Amplification), MALBAC (Multiple Annealing and Looping-Based Amplification Cycles), LIANTI (Linear Amplification via Transposon Insertion) and TnBC (Transposon Barcoded) have been the primary choices to prepare single-cell libraries. TnBC library prep method is a simple and versatile methodology, to detect copy number variations or to obtain the absolute copy numbers of genes per cell.

Molecular heterogeneity and early metastatic clone selection in testicular germ cell cancer development

Background

Testicular germ cell cancer (TGCC), being the most frequent malignancy in young Caucasian males, is initiated from an embryonic germ cell. This study determines intratumour heterogeneity to unravel tumour progression from initiation until metastasis.

Methods

In total, 42 purified samples of four treatment-resistant nonseminomatous (NS) TGCC were investigated, including the precursor germ cell neoplasia in situ (GCNIS) and metastatic specimens, using whole-genome and targeted sequencing. Their evolution was reconstructed.

Results

Intratumour molecular heterogeneity did not correspond to the supposed primary tumour histological evolution. Metastases after systemic treatment could be derived from cancer stem cells not identified in the primary cancer. GCNIS mostly lacked the molecular marks of the primary NS and comprised dominant clones that failed to progress. A BRCA-like mutational signature was observed without evidence for direct involvement of BRCA1 and BRCA2 genes.

Conclusions

Our data strongly support the hypothesis that NS is initiated by whole-genome duplication, followed by chromosome copy number alterations in the cancer stem cell population, and accumulation of low numbers of somatic mutations, even in therapy-resistant cases. These observations of heterogeneity at all stages of tumourigenesis should be considered when treating patients with GCNIS-only disease, or with clinically overt NS.

Symposium on single cell analysis and genomic approaches, Experimental Biology 2017 Chicago, Illinois, April 23, 2017

Emerging technologies for the analysis of genome-wide information in single cells have the potential to transform many fields of biology, including our understanding of cell states, the response of cells to external stimuli, mosaicism, and intratumor heterogeneity. At Experimental Biology 2017 in Chicago, Physiological Genomics hosted a symposium in which five leaders in the field of single cell genomics presented their recent research. The speakers discussed emerging methodologies in single cell analysis and critical issues for the analysis of single cell data. Also discussed were applications of single cell genomics to understanding the different types of cells within an organism or tissue and the basis for cell-to-cell variability in response to stimuli.