Genome profiles of pathologist-defined cell clusters by multiregional LCM and G&amp;T-seq in one triple-negative breast cancer patient

Zhu, Zhanping; Wang, Weiwei; Lin, Feng; Jordan, Tracy; Li, Guibo; Silverman, Sveta; Qiu, Si; Joy, Anil A.; Chen, Chao; Hockley, Deanna L.; Zhang, Xi; Zhou, Qing; Postovit, Lynne; Zhang, Xiuqing; Hou, Yong; Mackey, John R.; Li, Bo; Wong, Gane Ka‐Shu

doi:10.1016/j.xcrm.2021.100404

Cited by 5 publications

(3 citation statements)

References 66 publications

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“…Alternatively, by using laser capture microdissection (LCM)-based isolation of specific (single) cells from tissue sections, single-cell sequencing-based genome-plus-transcriptome or epigenome-plus-transcriptome profiling methods as described above can be applied at spatial resolution, as was done for analysing tumour development, metastasis and prognosis in patients with triple-negative breast cancer 114 .…”

Section: Spatial Multi-omics Methodsmentioning

confidence: 99%

Methods and applications for single-cell and spatial multi-omics

et al. 2023

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The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.

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Section: Spatial Multi-omics Methodsmentioning

confidence: 99%

Methods and applications for single-cell and spatial multi-omics

et al. 2023

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“…Low-throughput multimodal omics were successfully adopted in cancer studies to better understand heterogeneity, complexity, and the evolution of cancer cells by integrating genomic, epigenomic, and transcriptomic features of the same cells ( Macaulay et al, 2015 ; Hou et al, 2016 ; Bian et al, 2018 ; Zhu et al, 2021 ). However, the possibility of obtaining only tens or hundreds of cells provided by low-throughput multimodal omics and their costs have limited their application in TME studies.…”

Section: Single-cell Technologies To Study the Tmementioning

confidence: 99%

Breaking the Immune Complexity of the Tumor Microenvironment Using Single-Cell Technologies

et al. 2022

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Tumors are not a simple aggregate of transformed cells but rather a complicated ecosystem containing various components, including infiltrating immune cells, tumor-related stromal cells, endothelial cells, soluble factors, and extracellular matrix proteins. Profiling the immune contexture of this intricate framework is now mandatory to develop more effective cancer therapies and precise immunotherapeutic approaches by identifying exact targets or predictive biomarkers, respectively. Conventional technologies are limited in reaching this goal because they lack high resolution. Recent developments in single-cell technologies, such as single-cell RNA transcriptomics, mass cytometry, and multiparameter immunofluorescence, have revolutionized the cancer immunology field, capturing the heterogeneity of tumor-infiltrating immune cells and the dynamic complexity of tenets that regulate cell networks in the tumor microenvironment. In this review, we describe some of the current single-cell technologies and computational techniques applied for immune-profiling the cancer landscape and discuss future directions of how integrating multi-omics data can guide a new “precision oncology” advancement.

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“…Indeed, multiple displacement amplification (MDA) 6 drives the genomic amplification of G&T-seq and DR-Seq has genomic amplification uniformity comparable to that of MALBAC 7 , both of which are outperformed by PTA 1 in terms of genomic coverage, allelic balance and SNV calling metrics. Definition of clonal evolution at the SNV/CNV level in a primary patient sample has been accomplished utilizing G&T-seq, yet was limited to a candidate gene survey of exome-level data whereby clusters where defined by 59 oncogenes 8 and another studying employing G&T-seq limited their analysis to the RNA workflow of the method to take advantage of the low input requirement, without assessment of genomic level data 9 . Thus, we address here an unmet need to add genome-wide, high sensitivity and high precision SNV calling capability to a joint DNA/RNA single-cell methodology.…”

Section: Introductionmentioning

confidence: 99%

Unifying comprehensive genomics and transcriptomics in individual cells to illuminate oncogenic and drug resistance mechanisms

Zawistowski¹,

Salas-González²,

Morozova³

et al. 2022

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Discovering genomic variation in the absence of information about transcriptional consequence of that variation or, conversely, a transcriptional signature without understanding underlying genomic contributions, hinders understanding of molecular mechanisms of disease. To assess this genomic and transcriptomic coordination, we developed a new chemistry and method, ResolveOME, to extract this information out of the individual cell. The workflow unifies template-switching full-transcript RNA-Seq chemistry and whole genome amplification (WGA), followed by affinity purification of first-strand cDNA and subsequent separation of the RNA/DNA fractions for sequencing library preparation. In the ResolveOME methodology we leverage the attributes of primary template-directed amplification (PTA)1 to enable accurate assessment of single-nucleotide variation as a DNA feature—not achieved with existing workflows to assess DNA + RNA information in the same cell.We demonstrated the validity of the technique in the context of two major phenomena in oncology: tumor heterogeneity (leading to cancer progression) and treatment resistance. Material from a primary patient breast cancer and an acute myeloid leukemia (AML) cell line, MOLM-13, was used to highlight multiomic biomarker paradigms enabled by this chemistry. Performance of the PTA-enabled genome amplification was largely unaffected by addition of RNA enrichment, with control WGS results showing > 95% genome coverage, precision > 0.99 and allele drop out < 15%. In the RNA fraction of the chemistry, we were able to routinely retrieve full-length transcripts that demonstrate a ratio of 1 for 5’/3’ bias, with increased coverage of intronic regions and 5’ regions that are indicative of novel transcripts, showing strength of the template switching mechanism to capture isoform information with sparsity rates < 75%. We find remarkable cellular variability of revealed biomarkers at both in the genome and transcriptome despite employing a relatively small number of individual cells. In our primary patient sample of ductal carcinoma in situ (DCIS)/invasive ductal carcinoma (IDC) we observed oncogenic PIK3CA driver mutations and prototypical DCIS copy number alterations binned into heterogenous single-cell classes of genomic lesions. Within our quizartinib-treated MOLM-13 cells, we identified multiple potential mechanisms of resistance within seemingly sporadic changes and were able to associate specific mutation, copy number and expression significantly correlated to treatment. In this latter scenario, the DNA arm of our combined workflow uncovered a secondary FLT3 (non-internal tandem duplication (ITD)) mutation as a candidate primary driver of resistance to drug while the RNA arm showed matched transcript upregulation of AXL signal transduction as well as enhancer factor modulation. Importantly, proximal candidate regulatory SNVs, outside of the CDS, were identified and associated to upregulated transcripts in cis. The study highlights that both the genome and transcriptome are dynamic, leading to a set of combinatorial alterations that affect cellular evolution and that fate can be identified through ResolveOME application to individual cells.

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Genome profiles of pathologist-defined cell clusters by multiregional LCM and G&T-seq in one triple-negative breast cancer patient

Cited by 5 publications

References 66 publications

Methods and applications for single-cell and spatial multi-omics

Methods and applications for single-cell and spatial multi-omics

Breaking the Immune Complexity of the Tumor Microenvironment Using Single-Cell Technologies

Unifying comprehensive genomics and transcriptomics in individual cells to illuminate oncogenic and drug resistance mechanisms

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